While its widely accepted that the pharma industry is innovative in R&D, it is also true that it can be slow at embracing technological revolutions. Many people have criticized pharma companies for being slow to adopt AI. Indeed, some CEOs I talk to are concerned about too widely adopting AI, citing fears of unknown threats.

But as the CEO of Sanofi, I dont believe those challenges should guide our thinking or adoption of AI in the pharma business, as AI has the potential to improve and reinvent the way our business operates. AI impacts the way we exchange information by connecting different business units and functions that may have been operating independently in real time. The exchange of data in real time greatly accelerates and enhances the business operations.

But we have to be thoughtful about how we use AI. While some see AI adoption as a way to improve efficiency for example, through the acceleration and automation of repetitive tasks I think its real promise lies in insights and better decision intelligence, which will translate into better medicines, quicker, for the right patients, ultimately improving peoples lives.

Discovering innovative medicines is increasingly challenging, and the bar for differentiation and safety and efficacy is getting higher. Expert AI is about giving scientists the opportunity to benefit from massive computing power, machine learning, and trained algorithms for expanding the druggable universe. AI-enabled screening can sift through billions of possible molecules can allow R&D teams to shorten the search to find disease drivers and potential drug candidates. We can also be broader in the diseases we target for low incremental cost.

The development of expert AI for the discovery of new medicines will concern a relatively limited portion of the pharma workforce: those with highly specialized skills and knowledge at the intersection of biology, chemistry, data science and in collaboration with tech and startup companies.

But expert AI is not the only approach that pharma should take. The promise of using AI exists across the value chain of the industry as it candeliver insights and ultimately better outcomes, end to end across the enterprise. Thats the importance of fostering the use of snackable AI inside organizations.

Snackable AI is about applications that everyone across an organization can use on a daily basis. Apps can capture and aggregate a 360-degree view from the whole company, from finance to procurement and from supply to quality. Such apps can tell us what is going well and highlight where there are potentially problems. It can also give recommendations and nudge by suggesting next steps that can help fix an emerging problem thats useful at that moment in time. No individual could process all that information all at once.

Moreover, senior leadership can see the data at the same time as the teams, which means it is not polished or interpreted in advance, erasing levels of hierarchy in gleaning insights. Thankfully, it also means a very different way of working, with fewer Excel sheets and PowerPoint slides! In some cases, budgeting can entail thousands of slides; AI can cut that number to several dozen. We can eliminate wasted time. If we can democratize the data, there is less time spent in meetings and more focus on getting insights fast.

So, while expert AI is about giving to specialized R&D teams bespoke tools and technologies to find breakthroughs for patients, snackable AI is about democratizing access to companys data and helping the largest parts of an organization to make better everyday decisions. That will in turn lead to better allocation of resources and, ultimately, to better outcomes for patients.

The rapid progress of AI has triggered debate about the power of the technology, and risks. Of course, we have to have good governance, but we need to allow people the ability to play with AI in a curated environment. With the right guardrails in place, we can greatly enhance our decision intelligence and get breakthroughs and real added value. When people arent producingPowerPointor Excel sheets or debating the numbers, they can spend more time onseekingsolutions rather than simply trying to identify the problem.

The journey is as much cultural as technological. And it must start at the top. Most leaders of large organizations started their career in an analog world and must catch up to better master the basics of digital, data and artificial intelligence. Resistance to implementing AI can exist among teams for a number of reasons fear of process disruption, wrong decisions, and job elimination, to name a few. A key for greater adoption of snackable AI relies on a leaders ability to demonstrate how this new technology can help teams remove menial tasks and transactional work by focusing instead on ways to empower better decisions, founded on facts and less on emotions.

We need to get the base of our companies nudged into better decisions, every day, in a nonpolitical and nonconfrontational way. As we innovate with AI, the results achieved, such as more precise and better medicines and the possibility of operating in a more efficient way, will benefit many companies as the technology learns and improves. The advantage will come from those who operationalize it faster, who live it, because much of AI is about behavioral change. You can be slow and get replaced or act fast and be brave to make AI a key driver of progress and better decision intelligence in companies.

Paul Hudson is CEO of Sanofi.

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Snackable artificial intelligence, expert AI, and the pharma industry - STAT

DES MOINES, Iowa -- The cost of building an artificial intelligence product like ChatGPT can be hard to measure.

But one thing Microsoft-backed OpenAI needed for its technology was plenty of water, pulled from the watershed of the Raccoon and Des Moines rivers in central Iowa to cool a powerful supercomputer as it helped teach its AI systems how to mimic human writing.

As they race to capitalize on a craze for generative AI, leading tech developers including Microsoft, OpenAI and Google have acknowledged that growing demand for their AI tools carries hefty costs, from expensive semiconductors to an increase in water consumption.

But theyre often secretive about the specifics. Few people in Iowa knew about its status as a birthplace of OpenAI's most advanced large language model, GPT-4, before a top Microsoft executive said in a speech it was literally made next to cornfields west of Des Moines.

Building a large language model requires analyzing patterns across a huge trove of human-written text. All of that computing takes a lot of electricity and generates a lot of heat. To keep it cool on hot days, data centers need to pump in water often to a cooling tower outside its warehouse-sized buildings.

In its latest environmental report, Microsoft disclosed that its global water consumption spiked 34% from 2021 to 2022 (to nearly 1.7 billion gallons, or more than 2,500 Olympic-sized swimming pools), a sharp increase compared to previous years that outside researchers tie to its AI research.

Its fair to say the majority of the growth is due to AI, including its heavy investment in generative AI and partnership with OpenAI, said Shaolei Ren, a researcher at the University of California, Riverside who has been trying to calculate the environmental impact of generative AI products such as ChatGPT.

In a paper due to be published later this year, Rens team estimates ChatGPT gulps up 500 milliliters of water (close to whats in a 16-ounce water bottle) every time you ask it a series of between 5 to 50 prompts or questions. The range varies depending on where its servers are located and the season. The estimate includes indirect water usage that the companies dont measure such as to cool power plants that supply the data centers with electricity.

Most people are not aware of the resource usage underlying ChatGPT, Ren said. If youre not aware of the resource usage, then theres no way that we can help conserve the resources.

Google reported a 20% growth in water use in the same period, which Ren also largely attributes to its AI work. Googles spike wasnt uniform -- it was steady in Oregon where its water use has attracted public attention, while doubling outside Las Vegas. It was also thirsty in Iowa, drawing more potable water to its Council Bluffs data centers than anywhere else.

In response to questions from The Associated Press, Microsoft said in a statement this week that it is investing in research to measure AI's energy and carbon footprint "while working on ways to make large systems more efficient, in both training and application.

We will continue to monitor our emissions, accelerate progress while increasing our use of clean energy to power data centers, purchasing renewable energy, and other efforts to meet our sustainability goals of being carbon negative, water positive and zero waste by 2030, the company's statement said.

OpenAI echoed those comments in its own statement Friday, saying it's giving considerable thought" to the best use of computing power.

We recognize training large models can be energy and water-intensive" and work to improve efficiencies, it said.

Microsoft made its first $1 billion investment in San Francisco-based OpenAI in 2019, more than two years before the startup introduced ChatGPT and sparked worldwide fascination with AI advancements. As part of the deal, the software giant would supply computing power needed to train the AI models.

To do at least some of that work, the two companies looked to West Des Moines, Iowa, a city of 68,000 people where Microsoft has been amassing data centers to power its cloud computing services for more than a decade. Its fourth and fifth data centers are due to open there later this year.

Theyre building them as fast as they can, said Steve Gaer, who was the city's mayor when Microsoft came to town. Gaer said the company was attracted to the city's commitment to building public infrastructure and contributed a staggering sum of money through tax payments that support that investment.

But, you know, they were pretty secretive on what theyre doing out there, he added.

Microsoft first said it was developing one of the world's most powerful supercomputers for OpenAI in 2020, declining to reveal its location to AP at the time but describing it as a single system with more than 285,000 cores of conventional semiconductors, and 10,000 graphics processors a kind of chip that's become crucial to AI workloads.

Experts have said it can make sense to "pretrain" an AI model at a single location because of the large amounts of data that need to be transferred between computing cores.

It wasn't until late May that Microsoft's president, Brad Smith, disclosed that it had built its advanced AI supercomputing data center in Iowa, exclusively to enable OpenAI to train what has become its fourth-generation model, GPT-4. The model now powers premium versions of ChatGPT and some of Microsoft's own products and has accelerated a debate about containing AI's societal risks.

It was made by these extraordinary engineers in California, but it was really made in Iowa, Smith said.

In some ways, West Des Moines is a relatively efficient place to train a powerful AI system, especially compared to Microsoft's data centers in Arizona that consume far more water for the same computing demand.

So if you are developing AI models within Microsoft, then you should schedule your training in Iowa instead of in Arizona," Ren said. "In terms of training, theres no difference. In terms of water consumption or energy consumption, theres a big difference.

For much of the year, Iowa's weather is cool enough for Microsoft to use outside air to keep the supercomputer running properly and vent heat out of the building. Only when the temperature exceeds 29.3 degrees Celsius (about 85 degrees Fahrenheit) does it withdraw water, the company has said in a public disclosure.

That can still be a lot of water, especially in the summer. In July 2022, the month before OpenAI says it completed its training of GPT-4, Microsoft pumped in about 11.5 million gallons of water to its cluster of Iowa data centers, according to the West Des Moines Water Works. That amounted to about 6% of all the water used in the district, which also supplies drinking water to the city's residents.

In 2022, a document from the West Des Moines Water Works said it and the city government will only consider future data center projects" from Microsoft if those projects can demonstrate and implement technology to significantly reduce peak water usage from the current levels to preserve the water supply for residential and other commercial needs.

Microsoft said Thursday it is working directly with the water works to address its feedback. In a written statement, the water works said the company has been a good partner and has been working with local officials to reduce its water footprint while still meeting its needs.

-

O'Brien reported from Providence, Rhode Island.

The Associated Press and OpenAI have a licensing agreement that allows for part of AP's text archives to be used to train the tech companys large language model. AP receives an undisclosed fee for use of its content.

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Artificial intelligence technology behind ChatGPT was built in Iowa with a lot of water - ABC News

How should education change to address, incorporate, or challenge todays AI systems, especially powerful large language models? What role should educators and scholars play in shaping the future of generative AI? The release of ChatGPT in November 2022 triggered an explosion of news, opinion pieces, and social media posts addressing these questions. Yet many are not aware of the current and historical body of academic work that offers clarity, substance, and nuance to enrich the discourse.

Linking the terms AI and education invites a constellation of discussions. This selection of articles is hardly comprehensive, but it includes explanations of AI concepts and provides historical context for todays systems. It describes a range of possible educational applications as well as adverse impacts, such as learning loss and increased inequity. Some articles touch on philosophical questions about AI in relation to learning, thinking, and human communication. Others will help educators prepare students for civic participation around concerns including information integrity, impacts on jobs, and energy consumption. Yet others outline educator and student rights in relation to AI and exhort educators to share their expertise in societal and industry discussions on the future of AI.

Whether were aware of it or not, AI was already widespread in education before ChatGPT. Nabeel Gillani et al. describe AI applications such as learning analytics and adaptive learning systems, automated communications with students, early warning systems, and automated writing assessment. They seek to help educators develop literacy around the capacities and risks of these systems by providing an accessible introduction to machine learning and deep learning as well as rule-based AI. They present a cautious view, calling for scrutiny of bias in such systems and inequitable distribution of risks and benefits. They hope that engineers will collaborate deeply with educators on the development of such systems.

Jrgen Rudolph et al. give a practically oriented overview of ChatGPTs implications for higher education. They explain the statistical nature of large language models as they tell the history of OpenAI and its attempts to mitigate bias and risk in the development of ChatGPT. They illustrate ways ChatGPT can be used with examples and screenshots. Their literature review shows the state of artificial intelligence in education (AIEd) as of January 2023. An extensive list of challenges and opportunities culminates in a set of recommendations that emphasizes explicit policy as well as expanding digital literacy education to include AI.

Student and faculty understanding of the risks and impacts of large language models is central to AI literacy and civic participation around AI policy. This hugely influential paper details documented and likely adverse impacts of the current data-and-resource-intensive, non-transparent mode of development of these models. Bender et al. emphasize the ways in which these costs will likely be borne disproportionately by marginalized groups. They call for transparency around the energy use and cost of these models as well as transparency around the data used to train them. They warn that models perpetuate and even amplify human biases and that the seeming coherence of these systems outputs can be used for malicious purposes even though it doesnt reflect real understanding.

The authors argue that inclusive participation in development can encourage alternate development paths that are less resource intensive. They further argue that beneficial applications for marginalized groups, such as improved automatic speech recognition systems, must be accompanied by plans to mitigate harm.

Erik Brynjolfsson argues that when we think of artificial intelligence as aiming to substitute for human intelligence, we miss the opportunity to focus on how it can complement and extend human capabilities. Brynjolfsson calls for policy that shifts AI development incentives away from automation toward augmentation. Automation is more likely to result in the elimination of lower-level jobs and in growing inequality. He points educators toward augmentation as a framework for thinking about AI applications that assist learning and teaching. How can we create incentives for AI to support and extend what teachers do rather than substituting for teachers? And how can we encourage students to use AI to extend their thinking and learning rather than using AI to skip learning?

Brynjolfssons focus on AI as augmentation converges with Microsoft computer scientist Kevin Scotts focus on cognitive assistance. Steering discussion of AI away from visions of autonomous systems with their own goals, Scott argues that near-term AI will serve to help humans with cognitive work. Scott situates this assistance in relation to evolving historical definitions of work and the way in which tools for work embody generalized knowledge about specific domains. Hes intrigued by the way deep neural networks can represent domain knowledge in new ways, as seen in the unexpected coding capabilities offered by OpenAIs GPT-3 language model, which have enabled people with less technical knowledge to code. His article can help educators frame discussions of how students should build knowledge and what knowledge is still relevant in contexts where AI assistance is nearly ubiquitous.

How can educators prepare students for future work environments integrated with AI and advise students on how majors and career paths may be affected by AI automation? And how can educators prepare students to participate in discussions of government policy around AI and work? Laura Tyson and John Zysman emphasize the importance of policy in determining how economic gains due to AI are distributed and how well workers weather disruptions due to AI. They observe that recent trends in automation and gig work have exacerbated inequality and reduced the supply of good jobs for low- and middle-income workers. They predict that AI will intensify these effects, but they point to the way collective bargaining, social insurance, and protections for gig workers have mitigated such impacts in countries like Germany. They argue that such interventions can serve as models to help frame discussions of intelligent labor policies for an inclusive AI era.

Educators considerations of academic integrity and AI text can draw on parallel discussions of authenticity and labeling of AI content in other societal contexts. Artificial intelligence has made deepfake audio, video, and images as well as generated text much more difficult to detect as such. Here, Todd Helmus considers the consequences to political systems and individuals as he offers a review of the ways in which these can and have been used to promote disinformation. He considers ways to identify deepfakes and ways to authenticate provenance of videos and images. Helmus advocates for regulatory action, tools for journalistic scrutiny, and widespread efforts to promote media literacy. As well as informing discussions of authenticity in educational contexts, this report might help us shape curricula to teach students about the risks of deepfakes and unlabeled AI.

Students, by definition, are engaged in developing their cognitive capacities; their understanding of their own intelligence is in flux and may be influenced by their interactions with AI systems and by AI hype. In his review of The Myth of Artificial Intelligence: Why Computers Cant Think the Way We Do by Erik J. Larson, William Hasselberger warns that in overestimating AIs ability to mimic human intelligence we devalue the human and overlook human capacities that are integral to everyday life decision making, understanding, and reasoning. Hasselberger provides examples of both academic and everyday common-sense reasoning that continue to be out of reach for AI. He provides a historical overview of debates around the limits of artificial intelligence and its implications for our understanding of human intelligence, citing the likes of Alan Turing and Marvin Minsky as well as contemporary discussions of data-driven language models.

Gwo-Jen Hwang and Nian-Shing Chen are enthusiastic about the potential benefits of incorporating generative AI into education. They outline a variety of roles a large language model like ChatGPT might play, from student to tutor to peer to domain expert to administrator. For example, educators might assign students to teach ChatGPT on a subject. Hwang and Chen provide sample ChatGPT session transcripts to illustrate their suggestions. They share prompting techniques to help educators better design AI-based teaching strategies. At the same time, they are concerned about student overreliance on generative AI. They urge educators to guide students to use it critically and to reflect on their interactions with AI. Hwang and Chen dont touch on concerns about bias, inaccuracy, or fabrication, but they call for further research into the impact of integrating generative AI on learning outcomes.

Lauren Goodlad and Samuel Baker situate both academic integrity concerns and the pressures on educators to embrace AI in the context of market forces. They ground their discussion of AI risks in a deep technical understanding of the limits of predictive models at mimicking human intelligence. Goodlad and Baker urge educators to communicate the purpose and value of teaching with writing to help students engage with the plurality of the world and communicate with others. Beyond the classroom, they argue, educators should question tech industry narratives and participate in public discussion on regulation and the future of AI. They see higher education as resilient: academic skepticism about former waves of hype around MOOCs, for example, suggests that educators will not likely be dazzled or terrified into submission to AI. Goodlad and Baker hope we will instead take up our place as experts who should help shape the future of the role of machines in human thought and communication.

How can the field of education put the needs of students and scholars first as we shape our response to AI, the way we teach about it, and the way we might incorporate it into pedagogy? Kathryn Conrads manifesto builds on and extends the Biden administrations Office of Science and Technology Policy 2022 Blueprint for an AI Bill of Rights. Conrad argues that educators should have input into institutional policies on AI and access to professional development around AI. Instructors should be able to decide whether and how to incorporate AI into pedagogy, basing their decisions on expert recommendations and peer-reviewed research. Conrad outlines student rights around AI systems, including the right to know when AI is being used to evaluate them and the right to request alternate human evaluation. They deserve detailed instructor guidance on policies around AI use without fear of reprisals. Conrad maintains that students should be able to appeal any charges of academic misconduct involving AI, and they should be offered alternatives to any AI-based assignments that might put their creative work at risk of exposure or use without compensation. Both students and educators legal rights must be respected in any educational application of automated generative systems.

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September 10, (THEWILL) One of Africas foremost scientists, Professor Bart Nnaji, has advised Nigerians to embrace artificial intelligence (AI) on an industrial scale in order to join the 4th Industrial Revolution now sweeping across the globe.

Professor Nnaji, a former Minister of Science, made the appeal at the fifth convocation ceremonies of Michael and Cecilia Ibru University, at Agbara-Otor, near Ughelli. in Delta State where he also received an honorary doctorate in science.

AI has come to stay, he asserted before a large audience comprising academics and researchers from other universities, as well as business executives, philanthropists, and community leaders, including the founder of the university, Mrs. Cecelia Ibru, its vice-chancellor, Professor Ibiyinka Fuwape.

AI holds the key to our participation in the Fourth Industrial Revolution, driven by Big Data, Internet of Things, etc.

We lost the First Revolution which is the Agricultural Revolution, the Second which is the Industrial Revolution, and the Third which is the Digital Revolution.

Nnaji said that AI has become ubiquitous especially with Generative AI which enables machines, that is digital systems, to do things faster, cheaper and better through repetitive tasks and, in the process, achieve greater autonomy.

This means that they perform tasks without human control or human input, and this process keeps on improving rapidly.

He said that, unlike previous revolutions in history, Nigeria does not require massive resource infusion before leapfrogging into the 4th Industrial Revolution

The computer and the Internet have made things much cheaper, faster, and shorter, as a person can stay in the comforts of his or her home and still be in touch with cutting-edge technology, including AI, he declared.

While expressing delight that an increasing number of Nigerians are embracing AI, the erstwhile power minister advised the Nigerian government to immediately take concrete steps to make the country a significant AI participant, calling the United States, the United Kingdom, China, South Korea, the European Union, and India the frontline AI developers.

The Ministry of Communication and Creativity should be treated as a frontline development ministry, he argued, adding that the Nigerian Communication Commission and the National Office for the Acquisition of Technology should receive priority status.

He counselled the Federal Government to drastically reduce tariffs on certain information technology equipment or even abolish them.

He also called for intensive training of IT specialists in both academic and professional institutions in Nigeria and abroad.

He added: Let us borrow a leaf from India which prioritised Science, Technology, Engineering, and Mathematics (STEM) and has consequently excelled in medical tourism, manufacturing, food security, and moon and sun exploration.

Nnaji, however, pointed out some of the dangers associated with AI, including job losses and deep fakes.

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Nnaji Harps on Artificial Intelligence in 4th Industrial Revolution - THEWILL NEWS MEDIA

The energy sector must overcome a lack of trust in artificial intelligence (AI) before the technology can be effectively used to accelerate the energy transition, a DNV report has found.

Based on interviews with senior representatives from energy companies across the United Kingdom, DNVs research determined that while AI is already being used across the sector, companies are largely cautious of its new and unestablished uses. Interviewees include industry personnel from the Centre for Data Ethics and Innovation, EnQuest, National Gas, National Grid Electricity System Operator (ESO) and the Net Zero Technology Hub, among other organisations.

AI insights: Rising to the challenge across the UK energy system outlines how AI can contribute to the energy transition and that an industry-wide approach to standards and best practices is required to unlock its potential.

While AI can be key to advancement and innovation in energy supply chains, the research found that putting in place the foundations for trust in the providers of AI solutions and the outputs of those solutions must be prioritized in light of recent geopolitical events highlighting the need for countries to have energy sustainability, security and affordability in effect, a parallel trilemma for AI as it is increasingly democratized and utilized. It was also found that data policies and industry culture present significant barriers to its widespread adoption.

At industry level, data sharing has been identified as the area which requires the greatest improvement. In terms of culture, it was found that the engineering community has a high level of risk aversion and low tolerance to error.

Hari Vamadevan, Executive Vice President and Regional Director UK and Ireland, Energy Systems at DNV said: To truly harness the benefits of AI in the energy sector, its critical this technology is trusted. There are two main challenges in achieving this: information to evaluate the trustworthiness of an AI system, and communication, to relay evidence which allows users to trust the systems.

DNV has many years experience in AI and the latest in its suite of digital twins recommended practices now covers AI-enabled systems, providing a framework to assure those systems are trustworthy and managed responsibly throughout their entire lifecycle.

The emergence of artificial intelligence also poses cyber security risks in the sector, with heightened geopolitical tensions and the accelerating adoption of digitally connected infrastructure sparking concern over industrys vulnerabilities to cyber threats.

Shaun Reardon, Head of Section, Industrial systems, Cyber Security at DNV said: Accurate, accessible, reliable, and relevant digital technologies and AI tools must be all these things if we are to trust them. But they must also be secure. Digital technologies set to be enhanced by AI are being connected to control systems and other operational technology in the energy industry, where safety is critical. The industry needs to manage the cyber security risk and build trust in the security of these vital technologies.Source: DNV, https://www.dnv.com/news/artificial-intelligence-can-accelerate-the-energy-transition-but-must-gain-trust-of-the-sector-246640

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