Category: Ai

Built Robotics has introduced an autonomous pile driving robot that will help build utility-scale solar farms in a faster, safer, more cost-effective way, and make solar viable in even the most remote locations. Called the RPD 35, or Robotic Pile Driver 35, the robot can survey the site, determine the distribution of piles, drive piles, and inspect them at a rate of up to 300 piles per day with a two-person crew. Traditional methods today typically can complete around 100 piles per day using manual labor.

The RPD 35 was unveiled today at CONEXPO-CON/AGG in Las Vegas, the largest construction trade show in North America and held every three years.

The 2022 Inflation Reduction Act “Building a Clean Energy Economy” section includes a goal to install 950 million solar panels by 2030. With solar farms requiring tens of thousands of 12- to 16-foot-long piles installed eight feet deep with less than an inch tolerance, piles are a critical component of meeting that target.

Tyler Parker is Construction Tools and Technology Manager for Black & Veatch, a $4.3 billion Kansas City-based contractor that installed over 3.5 million kilowatts of solar by 2021, some of that with Built Robotics. ”Utility solar construction is ripe for disruption, and automation will be critical to get us to our clean energy goals,” he said. “Automation in solar farm construction — for example pile driving — frees up skilled labor to focus on more complex tasks and increases the speed and accuracy of building power infrastructure. Pile driving is a difficult and dangerous task, with tens or hundreds of thousands of piles to install on a single solar farm. Automation helps us build faster, safer and more accurately.”

Built Robotics founder and CEO Noah Ready-Campbell said, “The RPD 35 is our second fully-baked commercial product, after the our trenching product. Like many autonomy and robotics companies, we spent the first couple of years prototyping. The RPD 35 builds on our Exosystem autonomous platform, which is really the brain of the system. We use the Exosystem along with a number of other components to transform an excavator into a pile driving robot.”

Built Robotics made the strategic decision to focus on the solar industry rather than across multiple verticals. Ready-Campbell explained, “The demand for solar in particular is just off the charts. It’s very much becoming the dominant use case for us from a trenching standpoint. If we look forward over the next five or ten years, we think that it’s going to grow tremendously. The RPD 35 isn’t just an automation success story, but it’s also an electrification and renewable energy story.”

Construction labor shortages are significant, with The Homebuilder’s Institute’s 2022 Construction Labor Market Report estimating a shortage of 300,000 to 400,000 positions each month. An aging workforce and difficulty recruiting newcomers to the industry means that too many workers are leaving, and there aren’t enough new workers coming in. The result is not only project delays and cost escalation, but additional strain on existing workers that can lead to overwork, fatigue, worksite injuries, and those workers leaving construction. These shortages, and their impacts, are exacerbated at remote sites such as solar farms, threatening the nation’s ability to meet the Inflation Reduction Act’s goal.

In addition to alleviating the staffing shortage, construction automation is viewed by many as a means to keep workers safe while improving productivity. Automation and technology can also be used as a recruiting tool for digitally native younger workers to join an industry traditionally viewed as being very labor intensive and a challenging career choice. In fact, in a National Association of Home Builders survey, 63% of adults ages 18 to 25 who have not yet selected a career industry say there is little or no chance they would consider a construction career.

While construction robots first began showing up on the jobsite in the 1960s and 1970s, their use and applications have broadened in the recent past. Advanced Construction Robotics has a reinforcing bar (rebar) tying tool called TyBOT and a rebar carrying and placement robot called IronBOT. Built Robotics’ Exosystem is an aftermarket upgrade for excavators that allows them to operate autonomously. The company recently acquired Roin Technologies, known for its concrete robots. The National Aeronautics and Space Administration has even been developing autonomous technologies to build structures on the moon.

Despite the significant labor shortage, a natural tension exists about job security and workers being replaced by robots. Construction robotics companies are proactively addressing these concerns by focusing their efforts on repetitive and hazardous tasks. Construction Robotics offers a weightlifting robot and a semi-automated masonry (SAM) robot, and is quick to note, “There’s no substitute for experienced workers: we designed SAM to assist your team, not replace them. SAM reduces physical wear and tear, allowing employees to focus on exceptional results every day.” Built Robotics has even partnered with the International Union of Operating Engineers. Since 2020, the IUOE and Built Robotics have been working together to develop a new curriculum for autonomous training called a “Robotic Equipment Operator” for IUOE’s 400,000 members.

IUOE General President James Callahan asserts that training workers helps their careers, saying “Together with Built Robotics, we have pioneered a model of engagement between the Union and advanced technology providers to give our members continued opportunities to learn and develop their careers. Our partnership with Built has and continues to receive very positive feedback.”

Ready-Campbell adds, “Robots are just tools for the next generation of construction workers. If you look at the canal age, the Erie Canal was built in the 1800s with pickaxes, shovels, mules, and wheelbarrows. It was an extremely manual task. A few decades later you got to steam shovels. Fast forward again you got to diesel equipment, but it was cable-driven rather than hydraulic. And then in the 1970s and 1980s is really when you saw hydraulic equipment begin to dominate construction. So this has happened before and we’re just in the middle of the next renovation in construction equipment. We’re going from human-operated hydraulic equipment to computer-operated hydraulic equipment. If you look at it in that context, it doesn’t feel quite so new or quite so scary. It’s just the fourth wave of new means and methods that can be used for construction.”

The conversation has been edited and condensed for clarity. Check out my other columns here.

If you are 25 or younger, chances are high that you never encountered the paper version of Yellow Pages but throughout the 20th century, print directories were among the primary ways for consumers and businesses to connect. Established in 1886, Yellow Pages posted its final print issue in January 2019 closing the chapter on 130 plus history of print directory marketing. In the late 1990s, the new exotic profession of online directory marketing emerged with the rise of Yahoo! and other online directories, and quickly disappeared as the search engines took over. Search engine to be precise, since Google quickly took the lion’s share of the market in the early 2000s. Since then, every business is being bombarded by armies of search engine optimization (SEO) marketers offering to analyze and optimization of your websites, social networks, and all kind of tricks designed to get the business to the top of search results. According to Artios, there were over 200 thousand SEO professionals on LinkedIn alone and almost half of the people used a search engine when looking for products or services. With the advent of social networks, another notable profession emerged – Social Media Marketer (SMM). And many SEO “professionals” added SMM to their offerings.

At the end of 2022, OpenAI’s ChatGPT took the market by the storm beating Google at its own game in transformer neural networks, and triggering the massive transition from search to AI-generated answers and content. At present, Microsoft, Google, Amazon, and every other major technology giant is racing to advance its generative AI capabilities and introduce Large Language Models (LLM) and conversational AI into their products. This transition also elevated the many generative AI startups, including mine, which published in this area since 2016 and got the first drugs imagined by generative AI into the human clinical trials in 2021. Even for experts in this field, the release and rapid adoption of ChatGPT was largely unexpected and served as a call to accelerate and change. In order to stay competitive in generative AI, companies need to develop much better generative tools, produce specialized training data sets, train larger, more capable multi-modal systems, combine them with highly-accurate expert systems, incorporate expert human feedback, provide sticky user experience, and rapidly grow the user base. In this race for dominance, the developers often overlook the ability to integrate advertising and monetize the link between the customer and the product.

This rapid transition from search to generation coupled with the developers lack of clear guidelines for marketers poses is very confusing to the SOE and SMM marketers.

From SEO to GEO – What Can Marketers Do Today?

In this article, I speculate on some of the 5 steps SOE and SMM marketers can take to transition to Generative Engine Optimization (GEO).

1. Learn the Large Language Models

It is very difficult to optimize for something you do not understand. And understanding how generative systems work is essential. The systems that are most relevant to GEO are LLMs and multi-modal transformers.

I recommend read the first fundamental paper on transformer neural networks by Google titled “Attention is All You Need”. This title is has relevance to marketing as well, even though, in my opinion, is us usually the great product which is all you need.

Here are a few articles for the general audience that you may want to start with:

ChatGPT fundamentals by OpenAI

ChatGPT Explained by Towards Data Science

And for a slightly more advanced users, Andrej Karpathy, one of the heroes of the deep learning revolution, produced a very short youtube video showing how to quickly make your own GPT.

2. Monitor the Key Sources for Training Data

The current-generation of generative engines such as ChatGPT were trained on mostly publicly-available data coming from several corpuses of books, open repositories of articles, and Wikipedia.

Google’s crawling real-time crawling capabilities will provide it with a competitive advantage in its own generative systems development and it is surprising that it did not use its copy of the Internet to train a ChatGPT-like system. Most probably, it is due to the internal firewalls, copyrights and restrictions and the size and formatting of that data. However, it is reasonable to expect it to use this data in the future. But since the new generative tools work very differently from search and present the link functionality, most of the SOE techniques will no longer work. But to be good at GEO, marketers will need to focus on the most data-rich open copyright-free resources. These resources include Wikipedia, the many open-access publishers.

While it is arguably the largest volunteer-created source of human knowledge, Wikipedia has many biases, inaccuracies, and, previously, was not considered to be a reliable source by most academic institutions. Also, the articles that do not attract massive public attention can be easily manipulated by the tight group of senior editors. Ensuring accuracy of content in Wikipedia without breaching the rules, engaging in self-promotion or hiring paid help (even though it is allowed, the senior editors may discredit the edits by a person with the disclosed COI), is a challenge. Hopefully, the rise of generative AI will increase the activity in Wikipedia, improve governance, and improve the quality of content. Expert data curators should get more actively involved in Wikipedia editing and policing. New industry groups ensuring quality content are likely to be created and marketers may benefit by helping build and improve these groups.

3. Establish Working Relationship With the Key Generative AI Vendors

In the near future, Microsoft and other vendors that did not play in the search advertising business due to Google’s near-monopoly on search are likely to expand into generative engine marketing and explore the different advertising models. Following their thought leadership in the digital marketing space and participating in the early pilot projects will increase competitiveness in the future. These early experiments are likely to result in successful generative marketing startups that will inevitably be created in the next few years. By monitoring and collaborating with the generative AI vendors in the early days may help you become one of these successful generative marketing entrepreneurs.

4. Follow the Key Opinion Leaders (KOLs) in Generative AI and in Generative Marketing

While the field of generative engine optimization (GEO) and generative marketing is very new, the top people in the field. I recommend following:

• Sam Altman, the founder of OpenAI
• Andrej Karpathi, deep learning pioneer
• Ian Goodfellow, “father” of the generative adversarial networks
• Andrew Ng, one of the most prominent online AI educators
• Thang Luong, a scientist at Google Brain specializing in NLP
• Yann LeCun, one of the “fathers” of deep learning

I also recommend following KDnuggets and Data Science Central for borderline-technical content.

5. Get Better at Creating Unique Human-Generated Content

The text generation capabilities of ChatGPT are very impressive and it is possible to very rapidly generate human-like text for SEO, SMM, and other marketing purposes. However, most generative engine developers have already developed or are developing tools to detect machine-generated content. This content is likely to be discarded or deprioritized by the search engines, generative engines, publishers, and data providers. For example, Forbes.com already introduced new policies for the contributors prohibiting the use of generative tools for text generation. To prepare for the generative engine revolution it is important to get better at producing unique, valid, human-generated content that is more likely to capture the attention of the deep neural nets. Of course, there is also an opportunity in training the adversarial systems that would produce unique valid machine-generated content that would be indistinguishable from the original human content but I would leave this research direction to the experts. My advice to marketers is to avoid the generative digital pollution as it will decrease the quality of the AI systems in the short term and in bans and restrictions in the future.Consider Pursuing a New Career

With generative systems eating search and providing a more personalized and intimate experience for the users to connect with the products they need, the role of marketers in general may be deprioritized. Instead of pushing the products that consumers do not need it may be a good time to re-think your role in the world and consider creating or contributing to the creation of products and services that will positively impact human lives and the environment.

Microsoft shares rose 12.4% last week on word of the release of Copilot, a generative artificial intelligence tool to be integrated into its Office suite.

That’s huge news from the tech giant because it hands AI to the masses via highly familiar, everyday interfaces such as Word, Excel, PowerPoint and Outlook. Copilot will swiftly bring significant efficiencies and improvements to common tasks including email, analyses, business cases, presentations and performance reviews.

With streamlined workflows and automated administrative functions, companies will be challenged to rethink business models, talent needs and resource usage. Very quickly, it will spur creativity, shorten work timelines and improve results.

Yet, not all will benefit. Down the pipeline, that disruption may be more than many b-schools can handle — especially those burdened with antiquated curriculum, outdated delivery models, strained budgets, misaligned incentives, low student satisfaction, administrative bloat and workplace cultures not apt for innovation.

From a hiring perspective, employers may soon grow frustrated overpaying for a credential unaccompanied by skills that tomorrow’s competitive edge requires.

Into the void

The bifurcated MBA market faces an existential crisis. Top schools and their graduates remain in high demand. For instance, Northwestern’s Kellogg MBA reported 99% of its 2022 MBA graduates received job offers — with a median base salary of $165,000, up nearly 20% from 2019. Pay packages included hefty signing bonuses with consulting, tech and finance firms hiring nearly 80% of their graduates. That’s because Kellogg’s curriculum is built on applied learning, leading-edge technologies and meaningful corporate and executive partnerships.

Similarly, Stanford has deep, longstanding ties to the entrepreneurial tech community. Wharton leverages its elite executive programs in digital transformation, strategic finance and leadership for contemporary classroom experiences.

Many other programs are stuck, slumping or spiraling. In its 2022 Trends Report, the Graduate Management Admission Council found that 60% of MBA programs received fewer applications, with part-time programs seeing the most severe declines.

According to GMAC, post-pandemic interest in online, modern-day “correspondence course” offerings has been tepid, at best, with one-third of schools experiencing sizable application drops. Nearly 80% of surveyed respondents agreed that online programs lack adequate networking, educational value and career opportunities.

Those trends were before generative AI becomes mainstream. AI applications will heighten student expectations and end term papers, memorization and Industrial Revolution-style classroom lectures. Who can command tuition premiums without delivering more than what’s available for free? Is there any demand for hollow credentials? Are b-schools ready for such abrupt and transcendent change?

The clairvoyant

ChatGPT offered a first glance at that future, responding:

“Generative AI has the potential to significantly impact MBA education in a number of ways:

Customized learning: Generative AI can be used to create personalized learning experiences for MBA students. By analyzing data on students’ learning styles, preferences and performance, AI algorithms can generate customized content and activities to help each student learn more effectively.

Real-world simulations: AI can be used to generate realistic simulations of real-world business scenarios, allowing MBA students to practice decision-making and problem-solving in a safe and controlled environment.

Data analysis: AI can be used to analyze large datasets, allowing MBA students to gain insights into business trends and patterns that would be difficult or impossible to identify manually.

New teaching tools: AI-powered tools such as chatbots, virtual assistants, and intelligent tutors can provide MBA students with on-demand assistance and feedback, helping them to learn more efficiently and effectively.

Improved decision-making: By leveraging AI-powered analytics and insights, MBA graduates can make more informed and data-driven business decisions, leading to improved outcomes for themselves and their organizations.

Overall, generative AI has the potential to revolutionize MBA education by providing students with more personalized, data-driven and immersive learning experiences.”

How many schools can substantively address any of those five expectations? Very few. Kellogg has its Analytical Consulting Lab which enables students to solve real client problems such as guiding an NFL team’s concession pricing and formulating a a pharmaceutical firm’s sales plan. Lagging, legacy programs have wider gaps as they often rely on textbooks, yellowed lecture notes, obtuse theories and stale case studies (i.e. Enron) — quite the mismatch for an AI co-piloted world.

Weak hand

Innovative MBA curricula require difficult investment/divestment choices. Each is complicated by cash burn, rigid infrastructure, poor governance, tenured faculty, inflexible learning management systems, inexperienced academic leaders and competing campus priorities. That mix yields a worrisome, starting list of concerns:

• Sages off stage. Universities traditionally struggle to incentivize faculty to undertake curriculum innovation. Junior faculty have research aims to ensure tenure. Established professors leverage tenure to self-determine work priorities. Pressure to adapt decades-old teaching methods to the expectations of an AI world may speed many faculty to retirement, leaving b-schools with a sizable educational excellence talent deficit.
• New horizons. Market entrants are often underestimated by legacy enterprises. Emerging entities such as Section, Udemy and open-source learning platforms may now have the impetus to build coalitions, broaden educational offerings, develop innovative delivery methods and establish meaningful credentials. Whether partnering or competing with traditional schools, upstarts will shatter the status quo of business education.
• More co-pilots. To address data analytics demand, many graduate business programs teach coding. What will happen when popular software interfaces (i.e. Tableau, R, SAS, Power BI, Alteryx) embed AI? How will curricula cultivate the critical thinking and communication skills far beyond computations? Self-solving software upends credentialing-focused courses.
• Poor appetite. AI is far more than a new technology — it’s a monumental, transformational market force — the type that requires strategic agility, compelling leadership and novel solutions. That’s a particularly bad match for bureaucratic universities lacking meaningful incentives, curricular governance, unified stakeholders and novel tech teaching platforms.

MBA program futures rest on swift and substantive action to address these hurdles. In the next 1-3 years, AI will change education at all levels. The innovative will seize the spotlight and usher in a new era that readies students for dynamic futures. Laggards will grapple for relevance and existence. Who’s ready to take off the tweed and deal with the market reckoning?

Amid a flurry of Google and Microsoft generative AI releases last week during SXSW, Garry Kasparov, who is a chess grandmaster, Avast Ambassador and Chairman of the Human Rights Foundation, told me he is less concerned about ChatGPT hacking into home appliances than he is about users being duped by bad actors.

“People still have the monopoly on evil,” he warned, standing firm on thoughts he shared with me in 2018. Widely considered one of the greatest chess players of all time, Kasparov gained mythic status in the 1990s as world champion when he beat, and then was defeated by IBM’s Deep Blue supercomputer. That moment came to represent an awakening that machines might one day overpower humanity.

“AI wins not because it’s smarter than humans, but because it simply makes less mistakes,” he explained. “I never understood this fear of AI – it’s a useful tool that does what we tell it to do. I’m a big proponent of human-machine collaboration.”

What keeps him up at night is far more sinister, he said. In his role with the cybersecurity firm Avast, which was acquired in 2022 by NortonLifeLock Inc., and is now part of the Gen Digital family of products, the company found that while 30% of cyberattacks on its platform targets vulnerable networks with weak security, about 70% are phishing and other scams in which users are willingly giving their information to bad actors.

“These cognitive threats are a game changer and not something a firewall can be built against,” Gen Chief Technology Officer Michal Pechoucek told me. Social engineering attacks can be particularly effective when someone impersonates a close contact in your inner circle like your boss and instructs you to do something, like pay an invoice. With generative AI tools, it’s getting easier for scammers to write more convincing text because reinforcement learning is helping the algorithm fine-tune messaging to optimize the clickthrough rate. With all of the details of our day-to-day lives being shared across social networks, it’s also allowing for more nefarious personalization.

“It comes down to the consumer making the right decision,” Pechoucek said. Simple things people can do to protect themselves include checking the sender’s email to ensure the address matches the identity, not clicking on links or downloading PDFs, never reusing passwords, and sharing less on social media, he advised.

“It’s a lazy criminal going after a lazy customer,” Kasparov said. “Step up your defenses a little bit and they’ll move on.”

With regards to generative AI hacking into systems, Pechoucek said he was aware of a study by cyberthreat intelligence agency Check Point Research that showed ChatGPT being tricked into creating malware, going against its own rules. However, he said Gen uses generative AI in its own large language models to ensure its defense is strong enough to catch whatever new viruses criminals come up with.

Pechoucek says he can see a situation where people start to question the very reality of everything, especially with the advent of text-to-video deepfakes. One solution might be a platform where users can verify media with encrypted metadata, this could build digital safety and trust, he said.

Regardless of how innovation in the space plays out, Kasparov who ran as an opposition candidate to Russian president Vladimir Putin and left in exile a decade ago, said the US doesn’t have much to fear with regards to global cyber warfare with state-controlled adversaries. Assuredly he said, “Free people in free markets will always have the upper hand.”

Since the recent announcements of OpenView’s ChatGPT, and Google’s Bard, the industry has been in a frenzy advancing Generative AI products and solutions. Brainy Insights estimates that the generative AI market will grow from USD $8.65 billion in 2022 and reach USD 4188.62 billion by 2032. This translates to over 36% CAGR making generative AI one of the next hottest areas to elevate AI innovations. The software segment will account for the highest revenue share of 65.0% in 2021 and is expected to retain its position over the forecast period.

What is Generative AI?

Generative AI is a form of AI that produce various types of content including text, imagery, audio and synthetic data. The recent buzz around generative AI has been driven by the simplicity of new user interfaces for creating high-quality text, graphics and videos in a matter of seconds. Although not a new technology, the introduction of generative adversarial networks, or GANs which is a type of machine learning algorithm has advanced the innovations in using this form of AI.

COQUI – Generative AI will Revolutionize Voice

The exciting news is that former Mozillians have just raised $3.3M for Coqui, generative AI speech synthesis for all creatives. Prior to founding COQUI, the CEO Kelly Davis led the Mozilla Machine Learning Group, which focused on speech technology. Before that, he worked at the Max Plank Institute for Gravitational Physics and also did his Ph.D. work in Superstring Theory.

The company was founded in 2021 by Eren Gölge, Josh Meyer, Kelly Davis, and Reuben Morais, all whom worked at Mozilla’s machine learning group. Funding has come from leading players: ScaleX Ventures, Mango Capital, DNX Ventures, and angels. At Mozilla, they spent years working on speech technology but found traditional approaches to creating and controlling voices, at best, lacking and, at worst non-existent.

The Coqui founders have a bold strategy to provide generative AI voices for video game developers, audio post-production, and all creatives. When I asked Kelly, what his vision of the company was, he said in a few words, simply: Coqui wants to be Photoshop for Voice.

A bold vision but with what they have already germinated is very powerful as Coqui enables creatives to quickly and easily create, cast, and direct AI voice actors without all the overhead hassle. Users can easily create custom voices from a prompt, e.g., “Old man who smokes two packs a day”; cast out-of-the-box and custom voices in your projects; and their software directs every nuance of their performance. Coqui’s AI voices not only will save time, money, and headaches, drastically decreasing the time spent casting in the recording studio and also in post-production.

“We started Coqui because, using traditional approaches, we were spending months gathering custom voice data, weeks training custom voice models, and still found it impossible to direct every nuance of a voice’s performance. It was frustrating! There had to be a better way,” said co-founder and CEO Kelly Davis. “Later, we realized that everyone had the same problem! So, we rolled up our sleeves and got to work on a solution.”

For creatives, voice is a double-edged sword.

With the slightest shift in tone, it can paint the most detailed picture of our inner lives; however, it’s a nightmare to work with. Casting, recording, directing, scheduling, booking a studio, and doing it all again in post-production. Creatives crave a simple solution, and Coqui scratches that itch. Coqui provides high-quality, out-of-the-box AI voices; quick voice cloning; prompt-to-voice; and the ability to direct every nuance of a voice’s performance. It’s a single place for casting, recording, directing, and scheduling. Everything, all at your fingertips and all at the time and place of your choosing.

“After chatting to tons of creatives working on video games, audio post-production, dubbing, and lots of other disciplines, we know that the standard manta of casting, recording, directing, scheduling… is slowing development and costing time and money. Voice needs to be dragged into the 21st century, and generative AI is doing it,” says Kelly Davis, Co-Founder, and CEO of Coqui and previous Head of Mozilla’s Machine Learning Group.

The funding will be used to grow the sales and development teams and to accelerate growth in the US market.

The voice industry revolution is everywhere, and it’s a massive opportunity to lower production costs, accelerate development, and simply iterate faster. Coqui is bringing this revolution to voice. With high-quality, out-of-the-box AI voices; quick voice cloning; prompt-to-voice; and the ability to direct every nuance of a voice’s performance, Coqui is your on-ramp to voice’s generative revolution.

Summary

There is no question the voice revolution is underway and players like Coqui, although entering later that other industry players, like Altered AI, which provides speech-to-speech technology, Replica AI which provides game engine integration or Spotify, which recently acquired Sonantic also provides natural-sounding voices.

What stands out about Coqui is the founder’s depth of expertise in the voice and AI/ML field. Having such a tightly unified co-founding team gives them a glue edge that will hold them in good stead as they advance into the voice industry which requires major productivity (workflow process streamlining) improvements.

Roger Love, one of the most iconic voice leaders in the world (ie: trained Bradley Cooper to sing in A Star is Born, and helped Jeff Bridges win an academy win for his singing voice in Crazy Heart) is the CEO and co-founder of Emotional Cloud, a company using generative AI to enable more accurate man and machine and vice versa have a more emotionally relevant conversation. He is at the forefront of understanding voice cloning and understands that without the depth of emotional accuracy, these AI methods won’t truly advance human civilization, rather we could be at risk of eroding what is uniquely human.

Positive signs are that Coqui is paying special attention to emotional variance and valence in voice patterns.

That being said, there are still major risks for these types of disruptive voice innovations will impact jobs for voice actors, and other creatives. Yes there will be a greater efficiency for reducing costs and the voice industry is in need for a massive overhaul in multiple creatives world, from text to graphics, to video and voice – but there will also be an imbalance, unless we carefully ensure more social responsibility and industry transformation thoughtfulness.

This is not a new reality of disruptive innovations, but it is an area where increased ethical and responsible AI regulatory controls will be needed to ensure social responsibility is continually factored into all AI Industries.

Innovations like Coqui are creating sound waves and their efforts will no doubt leap frog ahead other industry players.

Note:

For additional insights on AI impacts in the music industry, see Dr. Cindy Gordon articles below.

Research Sources:

1. Brainy Insights. Generative AI Market Growth Report
2. Brooking Institute Research. Early Thoughts on Regulating Generative AI
3. Coqui WebSite.
4. Gordon, Cindy. Forbes Thought Leader Articles. AI impact on the Music Industry Article One, and Article Two.
5. Lawton, George. Everything you wanted to know about Generative AI, Tech Target

ChatGPT launched a tide wave of interest in AI. For many consumers, AI is finally living up to long overdue expectations. The accomplishments of ChatGPT in a short period of time are phenomenal. But what is yet to come when AI is combined with robotics will change everything.

I have been promoting the advances in robotics for several years. I even called 2022 the year of robotics, partially because of the growing need to overcome shortages in labor and to handle tasks beyond the physical or mental capability of humans, and partially because of the continued advances that AI, accelerated processing, semiconductor, sensors, wireless connectivity, and software technologies are enabling to develop advanced, autonomous machines. Robots are no longer just for the manufacturing floor. They are hazardous material handlers, janitors, personal assistants, food preparers, food deliverers, security guards, and even surgeons that are increasingly autonomous. Essentially, they are AI in the physical world. As a result, robot competitions are heating up from middle schools to Las Vegas.

As seen at CES, robotics technology is advancing rapidly with advances in technology. My favorite examples were the multi-configurable Yarbo outdoor robot and the John Deere See & Spray. Yarbo can be a mower, a leaf blower, or a snow blower. If it could dispose of animal excrement and the annoying neighbor, it would be perfect yard tool. On the other end of the spectrum was the John Deere See & Spray Ultimate, a tractor with up to a 120-foot (36.6m) reach that uses AI/ML to detect weeds smaller than the size of a smart phone camera and spray herbicide accordingly. John Deere also offers self-drive tractors.

There is a great deal of new technology going into these platforms and some of the latest of these technologies will be on display next week at another key AI technical conference – The GPU Technology Conference (GTC) – hosted by Nvidia. GTC started out as a gaming conference but has morphed into one of the premier AI conferences, of which a key component is robotics/autonomous machines.

One of the reasons for this transformation of GTC is Nvidia’s continued investment in both AI and robotics technologies, as well as in the ecosystem. At CES, Nvidia announced a new tool called Isaac Orbit for simulating environments and benchmarks for robot learning and motion planning. Nvidia also announced enhancements to Isaac SIM for reinforcement learning, collaborative programming, and realistic human simulation. Nvidia’s commitment to the segment is demonstrated in new announcements to the platforms and tools every few months. As with previous GTCs, we expect new platform and tool announcements next week.

In addition, GTC is offering a track just for robotic developers that will feature some of the applications using or being developed on the Nvidia platforms, current and future software tools, ecosystem partners and solutions, and new Isaac ROS (robot operating system) features for the open source community. These sessions include topics like “Training Highly Dynamic Robots for Complex Tasks in Industrial Applications” and “Design a Complex Robot Architecture on NVIDIA Isaac ROS.” Tracks will also include tutorials on how to use the various tools and the latest tool features for the Tao developer framework, Deepstream for AI vision applications, and the Isaac SIM for development, training, and testing. However, with all of Nvidia’s hardware, software, and tools built on the same AI technologies, other topics may be of interest to robotic developers, such as the Metropolis framework for AI video analytics and it’s Omniverse collaboration environment. Even the Nvidia GPU roadmap points the way to future Jetson platforms.

Unfortunately, this GTC will still be virtual, so we won’t get any of the up-close robot demos, but the event is free with open access to replay the on-demand sessions. The most important part of GTC for robotics is that the announcements at GTC set the stage for other announcements in robotics through the year.

The book “Written” penned by husband and wife writing team, Bec Evans and Chris Smith, is as much about understanding your own lifestyle as it is about finding your own writing style.

If we are in the post-modern, post-truth age then this is the post-normal book about how to get stuff written. It is not about an expert extolling advice from on high, the advice given is all about helping you to develop a system that works for you. The authors are very keen to stress that success will not come from imposing an unachievable way of working on everybody in a one-style-fits-all way. A successful writing project, or even writing career, comes down to noticing, appreciating, and focusing on exactly what works for you. “To thine own self be true”, as a famous writer once said, and that goes for your writing process too.

One of the most revealing sections is when Chris writes about the difference in working styles of the two authors, “…Bec told her family and friends about her writing – how much she was writing and how far along she was. She signed up to a #100daysofwriting challenge on Instagram and shared her writing progress with others in the community. She committed to writing times by joining online writing groups with London Writers’ Salon and also booked sessions with FocusMate. She’s been on writing dates with friends and booked a four-day writing retreat at a residential library. She joined a critiquing group where she shared her work-in-progress for feedback…She enlisted the support of beta readers to read the revised draft. She applied for and won a grant that supported her development and paid for her writing time”

Chris on the other hand says, “I sat at my desk, head down and wrote.”

This provides a flavour of the overall ethos but the book also includes all kinds of tools, support, initiatives and tips for writers too, however, everything is presented as very much a menu rather than a blueprint.

From how to find your own rules for writing, to goal setting, to help just getting started, to creating the optimum environment, and surrounding yourself with people that will hold you accountable, there is something in here for every budding writer – if you “know thyself” well enough.

Take procrastination for example, something I am tremendously partial to, and feel guilty about. Rather than focusing on the whole project and becoming intimidated, the authors advise on developing ‘starter steps’ to move towards the behaviour you want to build. For example, having a coffee or opening your notebook, or labelling a file. Perhaps use what they call a prompt, an everyday routine behaviour to which you can attach your newly desired behaviour so that may also become routine. A super example given in the book is that of Anthony Trollope, who would rise early and write every morning before work. Aided by his “old groom” who was paid to wake him with coffee. By beginning at that early hour with that recurring coffee, and at his desk by 5.30am, he could complete his literary work before he was dressed for breakfast.

The book does tackle the science of habits but the advice seems to be to create your own habitual routine that works for you because you are more likely to stick to it. One author cited uses a tracking tool dubbed the “Fitbit for writing” that asks writers to set a writing goal to track their progress. Others utilise treats or rewards to help encode new habits and hack the system. And the authors encourage would-be writers to identify ‘good things’ every time they write: “This is a type of cognitive restructuring that helps associate positive feelings with your work-in-progress rather than the default negative ones.”

There’s a lot more in the book besides, including many anecdotes from famous authors and personal stories about how to start to write, maintain your writing, build resilience in the face of inescapable adversity and even apparent failure. But my favourite section is on choice architecture and the insight into how Maya Angelou designed her writing approach around her own personal style. She would eschew all extraneous elements in her environment in order to focus on the task at hand, and start her day early, around 6am and after a coffee with her husband. She would then head off to a hotel room that she used as her workplace. It was stripped of all distraction, containing only a bed, a face basin, a dictionary, a Bible, a deck of cards and a bottle of sherry. “It’s lonely and it’s marvellous” she said.

The book is both a helpful toolkit as well as a story about how the book itself was written, and overall it reminds me of something Scott Adams also advocates which is to build systems not goals. The beauty of this book is that it guides you into finding the best writing process for you, one you can slide into as easily as a DM. Without building a system that supports you, you will find yourself fighting for goals you can never reach. And writing a book is hard enough. As the authors insightfully remark, “knowing what to write is important but that alone won’t get the writing done.”

Written: How To Keep Writing And Build A Habit That Lasts is published by Icon Books and is available in the US here and elsewhere in Europe on Amazon.

Two gladiators enter the ring and only one leaves.

I’m sure that you’ve heard or seen that famous line from time to time.

This handy trope implies that there can only be one winner. You either win or you lose. The winner survives and continues onward. The loser, well, we tend to assume they are left in the dust.

Some have been wondering whether this perhaps loosely describes the otherwise momentous event this week entailing the long-awaited and immensely anticipated unveiling and formal release of a generative AI app known as GPT-4.

You see, a lot of hype preceded the GPT-4 release. A lot of hype. Huge quantities. Gargantuan hype.

GPT-4 is essentially the successor to the widely and wildly popular ChatGPT. Both are products by AI maker OpenAI.

As I covered in my column coverage about the GPT-4 launch, see the link here, GPT-4 provides quite notable advances and improvements over ChatGPT. Pundits would generally agree that GPT-4 seems to be more fluent in its generative AI capabilities, appears to be faster, and has thankfully seemed to be architected to reduce though certainly not eliminate the otherwise frequent odds of generating factual errors, falsehoods, and so-called AI hallucinations (I don’t like the term “AI hallucinations” since it is a form of anthropomorphism, see my discussion at the link here, but it has caught on and we seem to be stuck with it).

In the months leading up to the GPT-4 release, the CEO of OpenAI Sam Altman repeatedly voiced his concern that the outsized hype was beyond the pale. All manner of zany speculation was being proclaimed in the media about what GPT-4 would contain. The belief was that GPT-4 would supposedly make our heads spin and be utterly revolutionary in showcasing the far reaches of what AI can accomplish.

Essentially, some outspoken outreaches of social media pushed a ludicrous agenda that any other AI maker might as well shut their doors and close their research labs because GPT-4 was going to be the ultimate cat’s meow. Perhaps, the media influencers fervently whispered, GPT-4 will embody Artificial General Intelligence (AGI), namely being sentient or fully human-like.

You might vaguely be aware that the CEO of OpenAI, Sam Altman, said this in an interview posted on YouTube (dated January 17, 2023): “The GPT-4 rumor mill is a ridiculous thing. I don’t know where it all comes from. People are begging to be disappointed and they will be. The hype is just like… We don’t have an actual AGI and that’s sort of what’s expected of us.”

You can perhaps sympathize with the OpenAI CEO. The rampant and unexpected success of ChatGPT had made the interest in its successor akin to the likes of a blockbuster movie. Having gotten lucky with the rollout of ChatGPT, a boon far beyond anyone’s expectations at the time, the world awaited the sequel with bated breath. Rumors were floating around that GPT-4 would knock our socks off and change the world as we know it.

Any CEO would usually welcome a tsunami of free publicity leading up to their product launch. My gosh, it is so hard to break through today’s news glut to get your product or service into the public eye. There are a zillion channels of news and a gazillion pitches for new products and services. In the case of GPT-4, there was a constant drumbeat of stay-tuned as to how society will be indubitably altered as a result of this vaunted successor to ChatGPT.

Even if you know your product is darned good and a cut above your prior product, trying to strive toward a hyped reality is something no CEO can delicately cope with. When ChatGPT came out, the expectations were quite low if not altogether nonexistent. ChatGPT ultimately far exceeded those initial expectations. On a scale of 1 to 10, ChatGPT was quietly beforehand somewhere near a 1 and after release was rushed into the stratosphere nearing an unbelievable and totally unforeseen 9 or 10. For my coverage of ChatGPT as a brand and a marketing bonanza, see the link here.

How can your successor product compete in the expectations gambit when compared to the surprising blockbuster status of your prior product?

The answer is that it probably can’t.

The best that you can do is attempt to calm down the expectations. We all know that in life the usual rule of thumb is to try and exceed expectations. That’s typically going to produce the highest outcome. The expectations for GPT-4 were so through the roof that almost by definition the reality would be less than the expectations. Thus, it makes sense to try and dampen down overly hyped expectations in such a circumstance.

You want to somehow attain the Goldilocks of expectations.

Allow me to explain.

The hope is to leverage any heightened expectations to ensure that your new product or service will be supremely newsworthy. Meanwhile, you want to watch out for a potential blowback if the rampant expectations are not met. People will say that the new product just didn’t cut the mustard. This can be exasperating when the new product is nonetheless stellar. It is only getting dinged because it didn’t meet some wide-eyed fictionalized high bar that no one could have ever met.

So, you try to do a gentle pushback at the sky-high expectations. You want to walk a fine line. Don’t push too hard to lower the bar cause people will think you’ve got a disappointment in the making. Also, you do want the eagerness to carry into the unveiling, thus, you have to keep the buzz alive. The soup has to be just the right temperature, not too hot and not too cold.

We now know the reality.

The reality is that GPT-4 seems to be more capable as a generative AI application and we ought to acknowledge and applaud the accomplishments thereof. That being said, the actual capabilities have not lived up to the hype. One doubts that there was ever any chance of doing so, not even the slimmest of chances that the real GPT-4 could match the hyped imagined farfetched GPT-4.

Into this comes those that are shocked and dismayed that GPT-4 hasn’t gotten as much attention as it presumably deserves. Sure, the news did cover the launch. But not in the over-the-top manner that many thought would occur. To the chagrin of some, the feeling is that the rest of the world just doesn’t realize how big a deal GPT-4 really is.

In today’s column, I address the key reasons that GPT-4 hasn’t taken the world by storm and become the dominant and all-encompassing mania that ChatGPT has exhibited. You would of course be hard-pressed to shed a tear over the coverage of GPT-4. GPT-4 has gotten a ton of press. No doubt about it. The essence here though is regarding those that believe the successor ought to be getting as much rabid focus as has ChatGPT. They ardently believe that this second blockbuster is a blockbuster and deserves the same immense accolades as the predecessor blockbuster ChatGPT.

Let’s unpack what is taking place.

Some of you might be wondering what in fact generative AI is. Let’s first cover the fundamentals of generative AI and then we can take a close look at the pressing matter at hand.

Into all of this comes a slew of AI Ethics and AI Law considerations.

Please be aware that there are ongoing efforts to imbue Ethical AI principles into the development and fielding of AI apps. A growing contingent of concerned and erstwhile AI ethicists are trying to ensure that efforts to devise and adopt AI takes into account a view of doing AI For Good and averting AI For Bad. Likewise, there are proposed new AI laws that are being bandied around as potential solutions to keep AI endeavors from going amok on human rights and the like. For my ongoing and extensive coverage of AI Ethics and AI Law, see the link here and the link here, just to name a few.

The development and promulgation of Ethical AI precepts are being pursued to hopefully prevent society from falling into a myriad of AI-inducing traps. For my coverage of the UN AI Ethics principles as devised and supported by nearly 200 countries via the efforts of UNESCO, see the link here. In a similar vein, new AI laws are being explored to try and keep AI on an even keel. One of the latest takes consists of a set of proposed AI Bill of Rights that the U.S. White House recently released to identify human rights in an age of AI, see the link here. It takes a village to keep AI and AI developers on a rightful path and deter the purposeful or accidental underhanded efforts that might undercut society.

I’ll be interweaving AI Ethics and AI Law related considerations into this discussion.

Fundamentals Of Generative AI

The most widely known instance of generative AI is represented by an AI app named ChatGPT. ChatGPT sprung into the public consciousness back in November when it was released by the AI research firm OpenAI. Ever since ChatGPT has garnered outsized headlines and astonishingly exceeded its allotted fifteen minutes of fame.

I’m guessing you’ve probably heard of ChatGPT or maybe even know someone that has used it.

ChatGPT is considered a generative AI application because it takes as input some text from a user and then generates or produces an output that consists of an essay. The AI is a text-to-text generator, though I describe the AI as being a text-to-essay generator since that more readily clarifies what it is commonly used for. You can use generative AI to compose lengthy compositions or you can get it to proffer rather short pithy comments. It’s all at your bidding.

All you need to do is enter a prompt and the AI app will generate for you an essay that attempts to respond to your prompt. The composed text will seem as though the essay was written by the human hand and mind. If you were to enter a prompt that said “Tell me about Abraham Lincoln” the generative AI will provide you with an essay about Lincoln. There are other modes of generative AI, such as text-to-art and text-to-video. I’ll be focusing herein on the text-to-text variation.

Your first thought might be that this generative capability does not seem like such a big deal in terms of producing essays. You can easily do an online search of the Internet and readily find tons and tons of essays about President Lincoln. The kicker in the case of generative AI is that the generated essay is relatively unique and provides an original composition rather than a copycat. If you were to try and find the AI-produced essay online someplace, you would be unlikely to discover it.

Generative AI is pre-trained and makes use of a complex mathematical and computational formulation that has been set up by examining patterns in written words and stories across the web. As a result of examining thousands and millions of written passages, the AI can spew out new essays and stories that are a mishmash of what was found. By adding in various probabilistic functionality, the resulting text is pretty much unique in comparison to what has been used in the training set.

There are numerous concerns about generative AI.

One crucial downside is that the essays produced by a generative-based AI app can have various falsehoods embedded, including manifestly untrue facts, facts that are misleadingly portrayed, and apparent facts that are entirely fabricated. Those fabricated aspects are often referred to as a form of AI hallucinations, a catchphrase that I disfavor but lamentedly seems to be gaining popular traction anyway (for my detailed explanation about why this is lousy and unsuitable terminology, see my coverage at the link here).

Another concern is that humans can readily take credit for a generative AI-produced essay, despite not having composed the essay themselves. You might have heard that teachers and schools are quite concerned about the emergence of generative AI apps. Students can potentially use generative AI to write their assigned essays. If a student claims that an essay was written by their own hand, there is little chance of the teacher being able to discern whether it was instead forged by generative AI. For my analysis of this student and teacher confounding facet, see my coverage at the link here and the link here.

There have been some zany outsized claims on social media about Generative AI asserting that this latest version of AI is in fact sentient AI (nope, they are wrong!). Those in AI Ethics and AI Law are notably worried about this burgeoning trend of outstretched claims. You might politely say that some people are overstating what today’s AI can do. They assume that AI has capabilities that we haven’t yet been able to achieve. That’s unfortunate. Worse still, they can allow themselves and others to get into dire situations because of an assumption that the AI will be sentient or human-like in being able to take action.

Do not anthropomorphize AI.

Doing so will get you caught in a sticky and dour reliance trap of expecting the AI to do things it is unable to perform. With that being said, the latest in generative AI is relatively impressive for what it can do. Be aware though that there are significant limitations that you ought to continually keep in mind when using any generative AI app.

One final forewarning for now.

Whatever you see or read in a generative AI response that seems to be conveyed as purely factual (dates, places, people, etc.), make sure to remain skeptical and be willing to double-check what you see.

Yes, dates can be concocted, places can be made up, and elements that we usually expect to be above reproach are all subject to suspicions. Do not believe what you read and keep a skeptical eye when examining any generative AI essays or outputs. If a generative AI app tells you that Abraham Lincoln flew around the country in his private jet, you would undoubtedly know that this is malarky. Unfortunately, some people might not realize that jets weren’t around in his day, or they might know but fail to notice that the essay makes this brazen and outrageously false claim.

A strong dose of healthy skepticism and a persistent mindset of disbelief will be your best asset when using generative AI. Also, be wary of potential privacy intrusions and the loss of data confidentiality, see my discussion at the link here.

We are ready to move into the next stage of this elucidation.

GPT-4 Post-Launch And What Has Been Happening

Let’s now dive into the ChatGPT successor, GPT-4.

Here are the main topics that I’d like to cover with you today:

• 1) The Reality Of What GPT-4 Is
• 2) Beloved Feelings Toward ChatGPT Are Hard To Reinvent
• 3) Tech-Awkward Naming Of GPT-4 Is A Big Catchiness Problem
• 4) Complications Of Ready Availability For GPT-4 Is An Added Complication
• 5) Cumbersome Compliment That Microsoft Bing Uses GPT-4
• 6) Higher Cost Of API Use For GPT-4 Is A Challenge
• 7) Being Able To Pass Examines Is Not Especially A Public Windfall
• 8) Multi-Modal Requires Delayed Gratification Right Now
• 9) Other

I will cover each of these important topics and proffer insightful considerations that we all ought to be mindfully mulling over. Each of these topics is an integral part of a larger puzzle. You can’t look at just one piece. Nor can you look at any piece in isolation from the other pieces.

This is an intricate mosaic and the whole puzzle has to be given proper harmonious consideration.

The Reality Of What GPT-4 Is

It seems that just about everyone vaguely knows something or another about ChatGPT (which is generally based on GPT-3.5). People were quite curious regarding the degree to which GPT-4, the ChatGPT successor, would compare on a head-to-head functionality basis.

Here’s what the official OpenAI blog posting about GPT-4 states:

• “In a casual conversation, the distinction between GPT-3.5 and GPT-4 can be subtle. The difference comes out when the complexity of the task reaches a sufficient threshold — GPT-4 is more reliable, creative, and able to handle much more nuanced instructions than GPT-3.5.”

The essence of that depiction is that you might not especially notice at first glance that GPT-4 is ostensibly more fluent, faster, and more AI-capable than ChatGPT. You might have to hunt for it to see the differences. You might via happenstance stumble into the differences. Otherwise, on ordinary day-to-day usage, you would generally have a tough time discerning whether you are using ChatGPT versus using GPT-4 (albeit notwithstanding the user interface differences).

That is somewhat of a problem when you are trying to get people excited about a new product.

When a new car comes out from a major carmaker, the odds are that the new features will be fully delineated and people will flock to see the difference between the prior model and the newest model. Gosh, the snazzy new dashboard is enthralling. Wow, the addition of heated bucket seats with an automatic seatbelt fastener is fascinating. Those sleek shapes of the front headlights that can pivot are quite a showstopper.

If the new features are nearly indistinguishable from the prior model, this is a devil of a situation to market to the public at large. How do you get people excited about under-the-hood advances? Our car goes from 0 to 60 mph in 3.4 seconds, while the prior model took 3.7 seconds. Few of the public will notice or care about the hidden ingenious changes made far within the guts of the vehicle.

In a sense, the same or similar predicament confronts GPT-4.

It isn’t easy or simple to point to substantive differences between ChatGPT and GPT-4. There are subtleties involved. Unfortunately, selling subtleties can be an uphill battle. Selling outright obvious and demonstrable differences is much easier (in a little while herein, I’ll be mentioning the multi-modal capability as a considered distinctive feature, which has some complications worthy of reviewing).

Returning to the hype that preceded GPT-4, a common contention was that GPT-4 would be completely free of any generated factual errors, biases, falsehoods, and AI hallucinations. This would be a gob-smacking accomplishment and would finally quiet all those doubters and skeptics that say they won’t use generative AI because of the inherent output-producing maladies.

Here’s what the official OpenAI GPT-4 Technical Report says about GPT-4:

• “GPT-4 has the tendency to ‘hallucinate,’ i.e. ‘produce content that is nonsensical or untruthful in relation to certain sources.’ This tendency can be particularly harmful as models become increasingly convincing and believable, leading to an overreliance on them by users. Counterintuitively, hallucinations can become more dangerous as models become more truthful, as users build trust in the model when it provides truthful information in areas where they have some familiarity.”

Thus, AI hallucinations are still in the game. The same goes for the outputting of factual errors, falsehoods, biases, and so on.

The good news is that strenuous efforts have been made by OpenAI and seem to be ongoing to try and reduce the chances of AI hallucinations in GPT-4. Also, the claim is made that GPT-4 outdoes GPT-3.5 in terms of averting AI hallucinations, even though OpenAI makes clear that they still are going to occur.

• “On internal evaluations, GPT-4-launch scores 19 percentage points higher than our latest GPT-3.5 model at avoiding open-domain hallucinations, and 29 percentage points higher at avoiding closed-domain hallucinations” (source: OpenAI GPT-4 Technical Report).

The thing is, though the indicated improvements are laudable, they are once again more so a type of under-the-hood advancement. If GPT-4 could have magically met the hyped expectations such that no akin ailments exist at all in GPT-4, that would have been astoundingly monumental.

And readily sellable.

To clarify, and to provide a balance to this consideration, no one has somehow cured this dilemma. Indeed, for those who are looking for hard AI problems, I urge you to jump into these waters and help out. There is plenty of work to be done on such matters.

Anyway, here’s another instance of something that turns out to not be particularly sellable.

First, please know that ChatGPT was data trained on data from the Internet and was capped or locked with data through 2021. This means that ChatGPT has no data per se for 2022 and 2023 in terms of events and activities after the year 2021. People using ChatGPT are often dismayed when they discover this data limitation.

The hype about GPT-4 was that it would be more up-to-date and would include 2022, and 2023, and possibly be working in real-time to adjust and encompass the latest postings on the Internet.

Here’s what the OpenAI GPT-4 Technical Report says:

• “GPT-4 generally lacks knowledge of events that have occurred after the vast majority of its pre-training data cuts off in September 2021 and does not learn from its experience. It can sometimes make simple reasoning errors which do not seem to comport with competence across so many domains, or be overly gullible in accepting obviously false statements from a user.”

Ergo, GPT-4 is also landlocked with data stuck in time.

I could go on and on about these aspects.

The emphasis is simply that there are few distinctive outright touchy-feely advances made in GPT-4 that can be readily seen and felt.

For those of you that favor GPT-4, and you certainly have good reasons to do so, I don’t want you to be leaping out of your chairs and exhorting that the aforementioned facets do a disservice to the advances made inside of GPT-4. I am merely pointing out that the real GPT-4 is a far cry from the hyped GPT-4, and that even the non-hyped GPT-4 is seemingly devised with few “sellable” distinctive features that would make it easier for the public at large to grasp why GPT-4 is so much better than ChatGPT.

Sometimes the sizzle is what sells the car.

An automotive engineer that works their heart and soul to improve an engine and drivetrain, allowing a car to speed up and shave a few split seconds on the 0 to 60 mph metric is likely a bit crestfallen when the public instead focuses on the shapely hood or the added roof rack. Sorry to say that the ornamented hood or the snazzy roof rack might end up being the keystone of why the car becomes a best seller, all else being equal.

That is human nature.

Beloved Feelings Toward ChatGPT Are Hard To Reinvent

Now that I’ve briefly covered the capabilities of GPT-4, let’s consider other factors that seem to underpin why GPT-4 is not as much a blockbuster in the eyes of the public as has been ChatGPT.

ChatGPT is the beloved AI that was the underdog and arose out of the blue.

The media hype about ChatGPT helped the OpenAI app become the darling of the world. People flocked to use it. ChatGPT became an overnight success. ChatGPT was heralded by the media and the public at large as an amazing breakthrough in AI.

The reality is that many other akin generative AI apps have been devised, often in research labs or think tanks, and in some cases were gingerly made available to the public. The outcome was not usually pretty. People prodded and poked at the generative AI and managed to get essays of an atrocious nature, see my coverage at the link here. The AI makers in those cases were usually forced to withdraw the AI from the open marketplace and revert to focusing on lab use or carefully chosen AI beta testers and developers.

Much of the rest of the AI industry was taken completely by surprise when ChatGPT managed to walk the tightrope of still producing foul outputs and yet not to the degree that public sentiment forced OpenAI to remove the AI app from overall access.

This was the true shock of ChatGPT.

Most people assumed the shock was the conversant capability. Not for those in AI. The surprise that floored nearly all AI insiders was that you could release generative AI that might spew out hateful speech and the backlash wasn’t fierce enough to force a quick retreat. Who knew? Indeed, prior to the release of ChatGPT, the rumor mill was predicting that within a few days or weeks at the most, OpenAI would regret making the AI app readily available to all comers. They would have to restrict access or possibly walk it home and take a breather.

The incredible success of the ChatGPT rollout has cautiously opened the door to other generative AI apps to also meet the street. For example, I’ve discussed the Google unveiling of Bard and how the Internet search engine wars are heating up due to a desire to plug generative AI into conventional web searching, see the link here.

Public sentiment about ChatGPT is incredibly strong, loyal, and so far long-lasting (I’ve discussed ways that this might eventually erode, see the link here).

We all especially seem to love those rags-to-riches stories. ChatGPT came out of nowhere. It was easy to use. It was fun to use. It amazed people with what it could do. It was made available for free. You could just go to a website, enter an email address, and voila, you were able to start using it. No hefty sign-up gauntlet. No slew of parameters to be set up. You simply log in, enter a prompt, and you get impressive essays as outputs.

Easy-peasy.

The positivity that has gone from 0 to 60 in just a few months as associated with ChatGPT is a marketer’s dream. Take any other product such as say the iPhone by Apple, Coke from Coca-Cola, and other generally prized products and compare their perceived positivity to ChatGPT.

Okay, so does GPT-4 have or will it attain a comparable semblance of being beloved as is ChatGPT?

Maybe, but probably not.

GPT-4 is not an underdog. It is not going to have the same widespread cultural impact that ChatGPT has had. This is not a fault of GPT-4 per se, and merely the circumstances of the luck of timing and moment-in-time phenomena that ChatGPT has encountered.

Without the same divine touching of our hearts, GPT-4 is going to just need to slog it out like any other product in the AI marketplace. That’s the real world.

Tech-Awkward Naming Of GPT-4 Is A Big Catchiness Problem

One of the luckiest or possibly shrewdest aspects of ChatGPT was the naming of the AI app.

Prior to ChatGPT, the prevailing names of the OpenAI line of text-based generative AI products had a predominantly techie phrasing to them, for example, GPT-2, GPT-3, and GPT-3.5 are the classic versioning type of names that technologists love to use (side note, another one, InstructGPT, veered more closely to something friendlier in naming).

If ChatGPT had gone to market with the name of say GPT-3.5 or maybe a step-up as GPT-3.6 (made this up), this type of naming could have somewhat undercut the resulting popularity of the AI app. Simply put, the wording of “Chat” when combined with “GPT” turned out to be a stroke of good fortune. It is easy to pronounce, readily memorable, catchy, and maybe even cuddly.

Most people don’t know what GPT is an acronym for (generative pre-trained transformer), and they don’t especially care anyway. They viscerally know it probably is something scientific or technical in whatever it does mean. The word “Chat” is well-known, and we today often associate it with an entire category of online products known as chatbots. By combining the two together into the coined ChatGPT, you end up with a clever combination. It has the aura of technology due to the GPT, and it has simplicity and transparency due to the word Chat. Voila, the name has likely indelibly entered into our modern-day lexicon.

What about the name of its successor, GPT-4?

Some had assumed that OpenAI would almost certainly concoct a new name for GPT-4, doing so just before the launch date. Probably all manner of expensive specialty product-naming companies would have been consulted. Secret surveys and test groups would be assembled. Nobody would know the final chosen name until the big reveal at launch.

And that name is GPT-4.

The key point is that rather than having a lovable huggable name, we are back to the usual techie predisposition of using the shall we say internal insider name. This is not endearing to the public at large. Without a catchy name, gaining traction in the minds and hearts of the public can be challenging.

Be aware that there are counterarguments on this point.

For example, suppose that OpenAI came up with some super-duper name. This could have added to the splash and splendor of the GPT-4 launch. On the other hand, it could also have the downside of creating potential confusion in the marketplace and possibly undercutting the ChatGPT name. Having two cutesy names might be overloading the public consciousness. Maybe it makes more sense to keep the ChatGPT as the spotlighted star, and then slip the other one, the more drab GPT-4 name, into the world and see how things go.

Some are trying to rectify this naming conundrum by making up names for GPT-4. For example, you might see from time to time the made-up reference to “ChatGPT-4” whereby agitators have decided to create a new name that seems more likable and associated with the predecessor brethren of GPT-4. Many other variations exist.

The official name is nonetheless GPT-4.

Not especially lovable.

Just a straight-ahead techie-sounding name.

Complications Of Ready Availability For GPT-4 Is An Added Complication

Another bit of blockage to stardom consists of the availability complications regarding GPT-4.

Recall that ChatGPT was available for free at the initial launch, and you could sign-up with great ease (they did put a momentary cap on signups due to the high volume, but that probably added to the allure and was not a dampening or dent in the popularity).

Do you know how to get access to GPT-4?

Let me share the complicated path with you.

Perhaps the CEO of OpenAI, Sam Altman, summarized it best in his tweet on March 14, 2023, about the GPT-4 launch:

• “Here is GPT-4, our most capable and aligned model yet. It is available today in our API (with a waitlist) and in ChatGPT+.”

As indicated, there are currently two main ways to get access to GPT-4.

One means consists of signing up for ChatGPT+ (also known as ChatGPT Plus). Here’s the deal. Conventional access to ChatGPT is still free if you do the ordinary access route. If you want some additional ChatGPT usage perks, you can subscribe at $20 per month and become a ChatGPT Plus member. As a ChatGPT Plus member, you also variously have access to GPT-4 (depending upon how busy GPT-4 is).

Bottom-line is that to use GPT-4, you pretty much have to be willing to shell out twenty bucks per month. This shows you that the AI industry is learning vital lessons from Netflix, Disney+, and the streaming world.

The other primary method of getting access to GPT-4 consists of connecting to GPT-4 programmatically via the available API (application programming interface). Generally, here’s how that works. Someone else that has a piece of software that wants to leverage GPT-4 can have their software connect to GPT-4 via the API. You might for example have a sales-oriented package that could fruitfully use the capabilities of GPT-4. As such, you modify your sales package to include an API to GPT-4. I’ve covered aspects of the ample business opportunities in leveraging the API of generative AI such as ChatGPT in the link here.

I’ll be saying more about the GPT-4 API in a moment below. Hang in there.

The crux of this is that trying to use GPT-4 is a pain in comparison to the ease of using ChatGPT. If you want to try out GPT-4, you have to jump through hoops. Basically, it isn’t readily available for free. This obviously can dampen the volume of anticipated usage for now.

There are counterarguments associated with this consideration.

For example, you might say it is quite clever to use GPT-4 as a lure to get people to sign-up for the $20 per month ChatGPT Plus. People that might have been thinking about getting ChatGPT Plus are now provided with a powerfully attractive added bonus for doing so. Those sitting on the fence are probably convinced to subscribe at this juncture.

Another seemingly canny element consists of preventing the entire world from all at once trying to use GPT-4. If GPT-4 had been completely free and easy to use, the chances are that tons and tons of people would have signed up. The resulting avalanche of overloading would almost certainly have meant that many people weren’t going to have an uninterrupted experience. Whiners would have gotten front-page headlines. Even if GPT-4 was a miracle AI system, the carping over it being slow, sluggish, and impossible to access would have drowned out any other accolades.

One might argue compellingly that providing some barriers to using GPT-4 was prudent and crafty. The formula is apparent. Take a small ding for not having unfettered access but avert a mitigated reputational destroying disaster if everyone did have access.

Cumbersome Compliment That Microsoft Bing Uses GPT-4

Shortly after GPT-4 was unveiled, word soon spread that the OpenAI AI product that had been earlier melded into the Microsoft Bing search engine activity was GPT-4. For my earlier coverage of the Bing search engine and the generative AI addition, see the link here.

Here’s the official Bing announcement made online on March 14, 2023:

• “Congratulations to our partners at Open AI for their release of GPT-4 today. We are happy to confirm that the new Bing is running on GPT-4, which we’ve customized for search. If you’ve used the new Bing preview at any time in the last five weeks, you’ve already experienced an early version of this powerful model. As OpenAI makes updates to GPT-4 and beyond, Bing benefits from those improvements. Along with our own updates based on community feedback, you can be assured that you have the most comprehensive copilot features available.”

Is that pronouncement about having included GPT-4 something to be proud of and ought to be touted from the highest hilltops?

Probably not, depending upon one’s viewpoint (I’ll explain next).

Here’s why.

You might have seen or heard in the news about all manner of crazy outputs that some have gotten the Bing search engine to devise when using the generative AI-added component. I’ve discussed how some people try to push the envelope and purposely get hate speech or other untoward outputs from generative AI, see the link here. There are those that do this to forewarn that generative AI is not ready for prime-time use. Others do it as a pastime or to garner attention. Etc.

The gist here is that Bing has already generally not had the utmost of reputations in terms of it being a search engine that has only a small portion of the search engine marketplace in comparison to Google, plus, the zany outputs that some got via the generative AI addition were also a bit of an embarrassment and letdown.

To associate the newly released GPT-4 with something that hasn’t gotten the most stellar of press is probably not the optimum advantage for the AI maker, even though it perhaps aids the search engine maker. Well, they are so closely allied now that it is probably a hair-splitting difference.

But I assume you get the essence of the predicament whereby GPT-4 garnered a somewhat cumbersome compliment.

Higher Cost Of API Use For GPT-4 Is A Challenge

I would guess that most people have no clue as to what the cost is to use the API for ChatGPT and nor what the cost to use the API for GPT-4 is.

There probably isn’t any notable reason they should need to know.

Only those that are desirous of using the APIs would likely figure this out. It is vitally important for them since they will need to somehow recoup the cost of using the APIs. If a sales package is going to use the API to access ChatGPT, this adds a new cost to the software maker of the sales package. They need to potentially increase their price to the users of the sales package to cover the cost or eat the cost in hopes of garnering added business for their sales package.

The reason I am going to bring it up here is that this is yet another small piece in the puzzle of what might keep GPT-4 from a lightning-neck pace of adoption. Money makes the world go round, and the same occurs if you want to use the API to connect to either ChatGPT or GPT-4.

Let’s first look at the API pricing for ChatGPT.

Per the official pricing on the OpenAI website, the cost to use the API of ChatGPT (known as gpt-3.5-turbo) is stated as $0.002 per 1,000 tokens.

That undoubtedly seems like a gobbledygook. A quick unpacking might help. When generative AI examines words, the words are divided into a set of tokens. Each token is roughly about 3 letters in size. The word “rabbit” which is six letters would generally be divided into two tokens “rab” and “bit”. In the English language, the average word length is about 4.7 letters. A handy rule of thumb is that you can multiply the number of tokens by approximately 75% to get the likely number of words. Thus, in the case of 1,000 tokens, the approximate equivalent is about 750 words.

Okay, with that under our belt, we will say that using the API of ChatGPT costs about $0.002 per 750 words. You might also find of interest that the average paragraph in the English language has around 200 words. So, for ease of discussion, let’s say that for $0.002 you can process about 4 paragraphs of normal size (750 / 200 = 3.75).

Things start to get a bit more complex as to how many paragraphs you might have when undertaking a written interactive dialogue. It all depends on what you are conversing about and how long of a conversation you want to have.

Imagine this. You start a conversation by entering a prompt that is a paragraph in size. ChatGPT emits an essay in response to your prompt. The essay is perhaps 5 paragraphs in size. You enter a new prompt that is one paragraph in size. ChatGPT responds with 6 paragraphs. You write two paragraphs as your next prompt. ChatGPT replies with 5 paragraphs. You close out the conversation.

At this juncture, you had consumed a total of 20 paragraphs, four at your prompts and sixteen as outputted by ChatGPT.

What did that cost?

If we assume that the price of $0.002 applies to about 4 paragraphs (roughly), and we used 20 paragraphs in this example, we know that the cost would approximately be (20 / 4) x $0.002 which is $0.01.

It cost a penny to use ChatGPT via the API for this rather short interactive conversation.

That seems like a heck of a deal. You can envision why many are flocking to add the use of ChatGPT to their software packages. Of course, you need to be mindful of this example. Suppose you have a user that is accessing the API via your software package and they carry on a full-blown written interactive conversation. Let’s suppose that might be a dime in cost.

If you have a thousand users of your package and they all are hourly using the API to the same degree as the dime, you are incurring perhaps a $100 added cost per hour for the ChatGPT usage. My point simply is that the numbers can add up due to volume and frequency. Be cautious to not get yourself into a bind.

I trust that this has whetted your appetite to know what the cost to use GPT-4 via the API is.

First, remember that I had just moments earlier stated that the official pricing to use the API of ChatGPT (known as gpt-3.5-turbo) is stated as $0.002 per 1,000 tokens.

The price to use GPT-4 is either $0.06 per 1,000 tokens for the 32K context, or it is $0.03 per 1,000 tokens for the 8K context (the context refers to the overall size of the contextual discussion taking place when interacting with GPT-4, wherein the larger the context the greater the cost).

An easier way to see these prices is side-by-side:

• $0.002 per 1,000 tokens (gpt-3.5-turbo)
• $0.060 per 1,000 tokens (GPT-4 at 32K context)
• $0.030 per 1,000 tokens (GPT-4 at 8K context)

This is quite a pricing leap.

Consider that the $0.002 pricing goes up by 30x to use the topmost GPT-4 (32K context) and goes up about 15x to use the other GPT-4 (8K context).

Another angle would be to say that my earlier example of a penny for using the API of ChatGPT would be thirty cents for the topmost GPT-4 (32K context) or around fifteen cents for the other GPT-4 (8K context). A thousand users on an hourly basis of added cost might jump from $100 per hour to $3,000 per hour or $1,500 per hour, though again that’s just one example and you need to consider the volume and frequency for whatever usage you have in mind.

I’ve noted in my column coverage on these matters that the decision to use the API of ChatGPT versus the API of GPT-4 is not a slam-dunk. You need to closely estimate the nature of the usage that might occur. You also need to weigh the advantages of GPT-4 over ChatGPT and whether the added cost is worth the benefits you expect to incur.

All in all, I’ve already predicted that we are going to see a flood of software that will use either ChatGPT or GPT-4 via the API. Some will probably start with ChatGPT to see how the marketplace reacts to the added connection in their package. At some later point, they are likely to upgrade to GPT-4. Others will potentially opt to skip past ChatGPT and decide that the cost is low enough overall to warrant the immediate aim of using GPT-4 and not play around with ChatGPT. If the potential profit or added market share to your package warrants the higher API cost, so be it.

Keep in mind that your mileage may vary and you need to dutifully figure out which path, if either one, makes sense for your software package. You ought to also look at other generative AI apps and review their pricing and features too.

Being Able To Pass Examines Is Not Especially A Public Windfall

We are getting toward the end of this list of points to consider, so I’ll speed things up.

You might have seen or heard that GPT-4 was able to pass various exams that are considered college-going or college-graduating types of tests. For example, GPT-4 was tried out on the SAT exam, the GRE exam (used for graduate college admissions), numerous high school AP exams that can earn college credits, and other exams. The most noted one of them all was probably the feared and esteemed bar exam that is customarily used to test lawyers when seeking to practice law (known as the Uniform Bar Exam). For my in-depth discussion on the bar exam passing and how this impacts lawyers and legal services, see the link here and the link here, just to name a few.

To those inside the AI industry, this was an impressive feat.

To the everyday public, this is somewhat impressive but maybe not quite as much as one might assume. People generally tend to think of “test-taking” as a somewhat narrow skill. Everybody knows that written tests are not the grand end-all. Sure, it is notable and possibly dramatic that a generative AI was able to do so well on those formidable exams. Nobody reasonably disputes that achievement.

The thing is, how does that translate into doing real-world tasks of a daily nature?

I would suggest that passing written exams is probably not going to win the hearts and minds of the public to the use of generative AI. It is a convenient metric for those in AI. It is measurable. You can report the results without much ambiguity. And so on.

For the public, likely “proof” of generative AI capabilities will require other or at least additional forms of triumphant accomplishments.

Multi-Modal Requires Delayed Gratification Right Now

I’ve predicted that this year we are going to see the advent of multi-modal generative AI, and it will astound and knock the socks off of everyone (for more on this, visit the link here).

Right now, we have essentially singular-mode generative AI.

For example, ChatGPT takes text as input and produces text as its output.

Think of it this way:

• ChatGPT Input: Text
• ChatGPT Output: Text

Simple enough.

Some generative AI apps take text as input and produce images or artwork as output. In that case, the input is just one mode, namely text, while the output is also just one mode, namely an image or artwork.

Think of it this way:

• Generative AI Input: Text
• Generative AI Output: Image or artwork

With me on this so far?

I hope so.

AI makers are burning the midnight oil to try and make their generative AI multi-modal.

The concept is simple. For input, you might have text, plus you might also have the entry of an image or artwork. That proffers two modes of input. The output might consist of generated text, plus a generated image or artwork, and let’s suppose a generated video too. That would be three modes of output.

The grandiose version of all feasible modes would be this:

• Generative AI Input: Text
• Generative AI Input: Image or artwork
• Generative AI Input: Audio
• Generative AI Input: Video
• Generative AI Input: Other modes
• Generative AI Output: Text
• Generative AI Output: Image or artwork
• Generative AI Output: Audio
• Generative AI Output: Video
• Generative AI Output: Other modes

That would be the pinnacle of multi-modal generative AI. You would have all modes available for input as prompts, and you would likewise have all modes available for the generated output. The user would be able to make their preferred choices, doing so at their whim.

This is where we are headed.

It is going to be breathtaking.

From an AI Ethics perspective, this is also going to be worrisome. If you thought deepfakes were a problem now, wait until we have full multi-modal generative AI. Hold onto your hats. You might also anticipate that lawmakers and regulators will inexorably be drawn into the generative AI marketplace when all sorts of unsavory sneakiness happen by evildoers exploiting the multi-model capabilities. New AI Laws will gain special urgency.

Back to the matter at hand, GPT-4 was launched and lauded as being multi-modal.

Here is what the official OpenAI GPT-4 Technical Report indicates:

• “We report the development of GPT-4, a large-scale, multimodal model which can accept image and text inputs and produce text outputs.”

In summary, you can enter text and you will get outputted text, plus you can possibly enter an image at the input.

This is what GPT-4 provides:

• GPT-4 Input: Text
• GPT-4 Input: Image or artwork (this functionality is not yet released to the public)
• GPT-4 Output: Text

Compare this to the grand aims of true multi-modal generative AI with all manner of inputs and all manner of outputs (i.e., GPT-4 is a rather abbreviated list at this time, as I’ll mention further in a moment).

Demonstrations showcasing the image or vision processing of inputted images have indicated that the items in a picture for example could be identified by the generative AI and then composed into a written narrative explaining the picture. You can ask the generative AI to explain what the picture seems to depict. All in all, the vision processing will be a notable addition.

The vision processing or image analysis capability is not yet available for public use (per the OpenAI website blog):

• “To prepare the image input capability for wider availability, we’re collaborating closely with a single partner to start.”

The crux of all of this is that it is heartwarming to realize that GPT-4 apparently does have the capability to do image input and analysis. Many are eagerly awaiting the public release of this feature. Kudos to OpenAI for nudging into the multi-modal arena.

So, we have text as input, plus image as input (when made available for public use), and text as output.

Some though have been handwringing in the AI community that this barely abides by the notion of multi-modal. Yes, there is one more mode, the image as input. But not an image as output. There seemingly isn’t audio as input, nor audio as output. There seemingly isn’t video as input, nor video as output. Those with a smarmy bent find this to be “multi-modal” in the most minimalist of ways.

The counterargument is that you have to crawl before you walk, and walk before you run.

We can ponder this seeming “multi-modal” capability from a skeptical or cynical perspective. Remember that I earlier said that when a carmaker comes out with a new car they want to have some shiny new features or functionality that can be marketed to the hilt. I also suggested that GPT-4 didn’t especially have any standout new features or functionality, at least none that would set it apart in a very distinctive way from ChatGPT in the eyes of the general public.

Aha, the provision of images as inputs is in fact the kind of added feature that would potentially stand out. A cynic might say that the AI maker nearly had to announce the image processing capability, regardless of what its status might be. This was the one new thing that they could hang their hat on and that would be a clear-cut standout from before. A cynic might further contend that showing demos is the classic demoware form of vendor mischief and so too is the deferring tactic of indicating that the feature is being polished before being released.

How much street cred or credit should be given to a product feature that is demonstrated but not released into the hands of the general public?

Some have insisted that this is perfectly acceptable, done all the time, and you have to simply hold your horses and wait for that delayed gratification to ensue.

One added twist is to contemplate the intriguing matter of what would have happened had the image processing been released at the time of launch. In a sense, that’s a difficult choice. It could propel the launched AI app to a greater feverish pitch as people scrambled to try this new nifty feature. The downside though is that since this is a new element and presumably untested by the public all told, the kind of zany things that people might have done could have become a public relations nightmare.

The thorny decision might have been that darned almost for sure if you do, but only slightly darned if you don’t.

Conclusion

Two gladiators enter the ring and both successfully walk out, shaking friendly hands. They are each and collectively entirely happy to be able to coexist.

That is a more fitting trope for the status of ChatGPT and the recent launch of GPT-4.

Some had wondered whether GPT-4 would instantly cannibalize the market for ChatGPT. If GPT-4 was leaps and bounds beyond ChatGPT, the belief was that ChatGPT would be left in the dust. On the other hand, and though not at all expected, if GPT-4 was somehow inferior to ChatGPT, the dust might be where GPT-4 would end up landing.

Nope, they each have their own particular tradeoffs, consisting of features and functions, along with differences in costs and availability.

If ChatGPT could talk, maybe it would be saying hello and warmly welcoming its brethren GPT-4. Likewise, GPT-4 would be complimenting ChatGPT on a job well done and appreciative of having set the stage for GPT-4 to enter into the real world from the labs that it has long been under construction.

A final remark for now.

Mother Teresa famously said this: “I can do things you cannot, you can do things I cannot; together we can do great things.”

At this time, it seems like the same could be said of the relationship between ChatGPT and GPT-4. They are respectful family members that each have their own path and particular destiny.

Irene Unceta is a professor and director of the Esade Double Degree in Business Administration & AI For Business

As machine learning progresses at breakneck speed, its intersection with sustainability is increasingly crucial. While it is clear that machine learning models will alter our lifestyles, work environments, and interactions with the world, the question of how they will impact sustainability cannot be ignored.

To understand how machine learning can contribute to creating a better, greener, more equitable world, it is crucial to assess its impact on the three pillars of sustainability: the social, the economic, and the environmental.

The social dimension

From a social standpoint, the sustainability of machine learning depends on its potential to have a positive impact on society.

Machine learning models have shown promise in this regard, for example, by helping healthcare organizations provide more accurate medical diagnoses, conduct high-precision surgeries, or design personalized treatment plans. Similarly, systems dedicated to analyzing and predicting patterns in data can potentially transform public policy, so long as they contribute to a fairer redistribution of wealth and increased social cohesion.

However, ensuring a sustainable deployment of this technology in the social dimension requires addressing challenges related to the emergence of bias and discrimination, as well as the effects of opacity.

Machine learning models trained on biased data can perpetuate and even amplify existing inequalities, leading to unfair and discriminatory outcomes. A controversial study conducted by researchers at MIT showed, for example, that commercial facial recognition software is less accurate for people with darker skin tones, especially darker women, reinforcing historical racial and gender biases.

Moreover, large, intricate models based on complex architectures, such as those of deep learning, can be opaque and difficult to understand. This lack of transparency can have a two-fold effect. On the one hand, it can lead to mistrust and lack of adoption. On the other, it conflicts with the principle of autonomy, which refers to the basic human right to be well-informed in order to make free decisions.

To promote machine learning sustainability in the social dimension, it is essential to prioritize the development of models that can be understood and that provide insights into their decision-making process. Knowing what these systems learn, however, is only the first step. To ensure fair outcomes for all members of society, regardless of background or socioeconomic status, diverse groups must be involved in these systems’ design and development and their ethical principles must be made explicit. Machine learning models today might not be capable of moral thinking, as Noam Chomsky recently highlighted, but their programmers should not be exempt from this obligation.

The economic dimension

Nor should the focus be solely on the social dimension. Machine learning will only be sustainable for as long as its benefits outweigh its costs from an economic perspective, too.

Machine learning models can help reduce costs, improve efficiency, and create new business opportunities. Among other things, they can be used to optimize supply chains, automate repetitive tasks in manufacturing, and provide insights into customer behavior and market trends.

Even so, the design and deployment of machine learning can be very expensive, requiring significant investments in data, hardware, and personnel. Models require extensive resources, in terms of both hardware and manpower, to develop and maintain. This makes them less accessible to small businesses and developing economies, limiting their potential impact and perpetuating economic inequality.

Addressing these issues will require evaluating the costs and benefits carefully, considering both short- and long-term costs, and balancing the trade-offs between accuracy, scalability, and cost.

But not only that. The proliferation of this technology will also have a substantial impact on the workforce. Increasing reliance on machine learning will lead to job loss in many sectors in the coming years. Efforts must be made to create new job opportunities and to ensure that workers have the necessary skills and training to transition to these new roles.

To achieve economic sustainability in machine learning, systems should be designed to augment, rather than replace, human capabilities.

The environmental dimension

Finally, machine learning has the potential to play a significant role in mitigating the impact of human activities on the environment. Unless properly designed, however, it may turn out to be a double-edged sword.

Training and running industrial machine learning models requires significant computing resources. These include large data centers and powerful GPUs, which consume a great deal of energy, as well as the production and disposal of hardware and electronic components that contribute to greenhouse gas emissions.

In 2018, DeepMind released AlphaStar, a multi-agent reinforcement-learning-based system that produced unprecedented results playing StarCraft II. While the model itself can be run on an average desktop PC, its training required the use of 16 TPUs for each of its 600 agents, running in parallel for more than 2 weeks. This raises the question of whether and to what extent these costs are justified.

To ensure environmental sustainability we should question the pertinence of training and deploying industrial machine learning applications. Decreasing their carbon footprint will require promoting more energy-efficient hardware, such as specialized chips and low-power processors, as well as dedicating efforts to developing greener algorithms that optimize energy consumption by using less data, fewer parameters, and more efficient training methods.

Machine learning may yet contribute to building a more sustainable world, but this will require a comprehensive approach that considers the complex trade-offs of developing inclusive, equitable, cost-effective, trustworthy models that have a low technical debt and do minimal environmental harm. Promoting social, economic, and environmental sustainability in machine learning models is essential to ensure that these systems support the needs of society, while minimizing any negative consequences in the long term.

Shares of billionaire Robin Li’s Baidu, which tumbled 6.4% on Thursday on disappointment over the launch of its ChatGPT-like service, surged almost 14% Friday as some analysts who tried Ernie Bot gave favourable reviews.

Hong Kong-listed Baidu rose HK$17.10 to close at HK$142.20.

The source of Thursday’s market reaction was that the highly-anticipated launch of the service involved a series of pre-recorded videos instead of any real-time performance.

On Friday, analysts from Citigroup said in a research note that while Ernie Bot wasn’t perfect, it was able to answer the majority of complicated or absurd questions put forward. They also described Thursday’s share price drop as an overreaction from the market.

Baidu in February confirmed its plan to roll out the ChatGPT-like service, becoming the first in China to make available a rival to the artificial intelligence-powered chatbot developed by San Francisco-based OpenAI. The Baidu has said that Ernie Bot excels at areas including business writing and understanding the Chinese language.

Thursday’s launch of Ernie Bot came just two days after Microsoft-backed OpenAI unveiled ChatGPT-4. The latest version of the highly popular AI chatbot features some more powerful functions, such as processing not just text but also images.