"Computers need to be accountable to machines," a top Microsoft executive told a roomful of reporters in Washington, DC, on February 10, three days after the company launched its new AI-powered Bing search engine. Computers need to be accountable to people!" he said, and then made sure to clarify, "That was not a Freudian slip." Slip or not, the laughter in the room betrayed a latent anxiety. Progress in artificial intelligence has been moving so unbelievably fast lately that the question is becoming unavoidable: How long until AI dominates our world to the point where we're answering to it rather than it answering to us? First, last year, we got DALL-E 2 and Stable Diffusion, which can turn a few words of text into a stunning image. Then Microsoft-backed OpenAI gave us ChatGPT, which can write essays so convincing that it freaks out everyone from teachers (what if it helps students cheat?) to journalists (could it replace them?) to disinformation experts (will it amplify conspiracy ...

Recently, OpenAI, an AI research laboratory based in San Francisco, announced the launch of its latest AI chatbot, GPT-4. This advanced chatbot has the capability of handling both text and image input, making it more technologically advanced than its predecessor, GPT-3.5. The launch of GPT-4 is expected to usher in a new era of artificial intelligence and its impact on the world. According to OpenAI, GPT-4 is more creative and collaborative than its predecessor, ChatGPT, which was released in 2022. It can handle multiple tasks, such as generating, editing, and collaborating with users on various technical and creative writing tasks, including composing songs, creating screenplays, and analyzing the writing style of a user.

Mitacs recently asked two leading experts to talk about the intersection of artificial intelligence and clean technology. Ivette Vera-Perez is the Team Lead for Mitacs's Account Management group with expertise in clean technology. Ivette serves on the board of directors of the Ontario Clean Technologies Industry Association (OCTIA).Tibor Turi is the executive director of SOSCIP, a consortium of academic and industry members that supports collaborative research projects through partnership-building services and access to leading-edge advanced computing platforms. Tibor: The current state of AI serving cleantech is both exploratory and operational. It is exploratory because cleantech and AI are both interested in solving critical real-world problems that push researchers and entrepreneurs right to the very edge of innovation.

Like transformative technologies that came before, the ethics of AI is coming under increased scrutiny, giving birth to regulations and policies constraining the scope of its application. Artificial Intelligence is a dynamic technology sector, powering a broad spectrum of emerging applications in the fields of industrial robotics and robotic process automation (RPA). Like transformative technologies that came before, the ethics of AI is coming under increased scrutiny, giving birth to regulations and policies constraining the scope of its application. What are the risks and what's being done to promote an ethical application of one of today's most empowering technologies? What exactly is AI and what role does it play in robotic technologies?

Members of IDG's Influencer Network weigh in on the transformative power of these two technologies. As a recent article on CIO.com observed, the pandemic "has seen accelerated interest in process automation as organizations have scrambled to overhaul business processes and double down on digital transformations in response to disruptions brought about by COVID-19. And for IT leaders stepping into or already steeped in such modernization efforts, artificial intelligence -- mainly in the form of machine learning -- holds the promise to revolutionize automation, pushing them closer to their end-to-end process automation dreams." Automation and artificial intelligence (AI): The combination of these two transformative technologies has IT leaders setting their sights on some pretty lofty goals. Robotic process automation leader UiPath has characterized RPA and AI as "two of the most transformative technologies the world has ever known. But bringing AI and RPA together unleashes even more of their potential."

For the insurance industry, the health and safety benefits of wearables and other IoT-connected devices is well established. But meeting new customer demands for protection goes beyond capturing user-generated data. What matters now is how an insurer and their ecosystem partners use the data shared with them by the customer. And whether they have the right mix of talent and technology to optimize its use. Analytics capabilities, including predictive and prescriptive analytics, can enable data-driven insurance offers in real-time.

Few would dispute the idea that artificial intelligence will be a transformative technology for financial services. Yet the view of how that transformation will shake out may be evolving significantly. A report from Deloitte and the World Economic Forum contends that in the near future, technology expertise will grow so commonly available that raw AI and multiple technologies built around that hub will not be what separates the winners from the other players. Instead, as envisioned by the report, the transformative technologies that excite so many today will become as basic to the industry as the longstanding payments rails they all share today. What institutions do with that transformative technology will mean much more and that will hinge on some surprisingly basic ideas.

How can your company capitalize on this dynamic technology? This program views the technical elements of machine learning through the lens of business and management, and equips you with the relevant knowledge to discover opportunities to drive innovation and efficiency in your organization. Although you can expect to explore technical aspects of machine learning, the focus is on empowering you, as a business leader, to ask the right questions about whether machine learning applications will benefit a particular business problem, or make your organization more efficient. Through a mix of research insights reinforced by case examples, you'll have the opportunity to critically apply your learning. You'll learn to identify the realistic opportunities of this transformative technology as you develop an implementation plan for machine learning in a business of your choice.

Anyone observing the news can see that artificial intelligence and machine learning have been getting lots of attention for the past few years. It goes without saying that startups are playing into this trend and raising more money than ever, as long as they have AI or cognitive technologies in their business plans or marketing material. Not only are startups raising increasingly eye-opening amounts of money, but venture capital (VC) funds themselves are raising skyrocketing levels of new capital if they focus their portfolios on AI and related areas. But are we in a bubble? Are these VC investments in AI realistic or out of control?

Recently the concept of transformative AI (TAI) has begun to receive attention in the AI policy space. TAI is often framed as an alternative formulation to notions of strong AI (e.g. artificial general intelligence or superintelligence) and reflects increasing consensus that advanced AI which does not fit these definitions may nonetheless have extreme and long-lasting impacts on society. However, the term TAI is poorly defined and often used ambiguously. Some use the notion of TAI to describe levels of societal transformation associated with previous 'general purpose technologies' (GPTs) such as electricity or the internal combustion engine. Others use the term to refer to more drastic levels of transformation comparable to the agricultural or industrial revolutions. The notion has also been used much more loosely, with some implying that current AI systems are already having a transformative impact on society. This paper unpacks and analyses the notion of TAI, proposing a distinction between TAI and radically transformative AI (RTAI), roughly corresponding to societal change on the level of the agricultural or industrial revolutions. We describe some relevant dimensions associated with each and discuss what kinds of advances in capabilities they might require. We further consider the relationship between TAI and RTAI and whether we should necessarily expect a period of TAI to precede the emergence of RTAI. This analysis is important as it can help guide discussions among AI policy researchers about how to allocate resources towards mitigating the most extreme impacts of AI and it can bring attention to negative TAI scenarios that are currently neglected.