Fox News host Steve Hilton delves into ChatGPT, an artificial intelligence program that could have major implications for writing-focused jobs on'The Next Revolution.' Quickly evolving artificial intelligence technologies like ChatGPT could increase cyberattacks from foreign countries and displace workers in the U.S. labor force, highlighting the need for new skills and training among American students and workers, according to experts. Netra AI CEO Don Horan noted that artificial intelligence could be used to generate malicious code quickly by removing the algorithms' intended controls and creating content outside the authorized purview. He said that foreign acts can utilize tools like ChatGPT to improve espionage and accelerate elicitation, a process wherein a perpetrator gets to know a subject very well by gathering information and creating "the profile of a human being." This information is then used to force people to comply with their intended mission.

Welcome to the latest issue of your guide to AI, an editorialized newsletter covering key developments in AI research, industry, geopolitics and startups during January 2023. This one is a monster so it might get clipped in your inbox (read the online version in case!). Nathan wrote an oped in The Times for why university spinouts are a critical engine for our technology industry and why spinout policy needs urgent reform. The Times Higher Education profiled our open source data term database, spinout.fyi. Nathan commented on The Financial Times' Big Read on The growing tensions around spinouts at British universities. The State of AI Report provided two key figures to The Economist's piece on The race of the AI labs heats up. Register for next year's RAAIS, a full-day event in London that explores research frontiers and real-world applications of AI-first technology at the world's best companies. As usual, we love hearing what you're up to and what's on your mind, just hit reply or forward to your friends:-) BioNTech acquired London and Tunis-based AI startup InstaDeep for $680M (cash stock) - this was a huge deal.

Artificial intelligence (AI) is rapidly transforming how businesses operate and interact with their customers. From automating routine tasks to transforming decision-making processes, AI has the potential to significantly improve efficiency and effectiveness in the business world. In this article, I will discuss how AI is shaping the future of business and provide examples of some companies that are already implementing these technologies. Automation is likely to be the only major way AI will shape the future of business. This has many implications for automating business activities and affects a wide range of industries.

The world's largest companies cannot be given free rein in their competition to capitalise artificial intelligence. What is the best way to develop artificial intelligence? This question, long theoretical, is quickly becoming a hands-on concern, which will soon demand that important strategic choices be made. We are seeing two completely different approaches play out before our eyes. One is the race among global technology giants which began with the recent launch of the Microsoft-funded ChatGPT, already provoking promises of similar systems from Google and the Chinese company Baidu.

Even if you aren't into artificial intelligence, pay attention, because this one is a big deal. The tool, from a power player in artificial intelligence called OpenAI, lets you type natural-language prompts. ChatGPT then offers conversational, if somewhat stilted, responses. The bot remembers the thread of your dialogue, using previous questions and answers to inform its next responses. It derives its answers from huge volumes of information on the internet. ChatGPT is a big deal. The tool seems pretty knowledgeable in areas where there's good training data for it to learn from.

At Microsoft, we believe artificial intelligence (AI) is the defining technology of our time. We have been on the forefront of cutting-edge research in AI and integrating these powerful, innovative AI technologies into our products and services to help customers do more. Microsoft AI, powered by Azure, provides billions of intelligent experiences every day in Windows, Xbox, Microsoft 365, Teams, Azure AI, Power Platform, Dynamics 365 and Microsoft Defender. Our AI tools and technologies are designed to benefit everyone at every level in every organization. They are used in workplaces, home offices, academic institutions, research labs and manufacturing facilities around the world, and they are helping everyone from scientists and salespeople to farmers, software developers and security practitioners.

"Artificial intelligence is the future, not only for Russia, but for all humankind. It comes with colossal opportunities, but also threats that are difficult to predict. Whoever becomes the leader in this sphere will become the ruler of the world."1 "A people that no longer can believe anything cannot make up its mind. It is deprived not only of its capacity to act but also of its capacity to think and to judge. And with such a people you can then do what you please."2

While humans can extract information from unstructured text with high precision and recall, this is often too time-consuming to be practical. Automated approaches, on the other hand, produce nearly-immediate results, but may not be reliable enough for high-stakes applications where precision is essential. In this work, we consider the benefits and drawbacks of various human-only, human-machine, and machine-only information extraction approaches. We argue for the utility of a human-in-the-loop approach in applications where high precision is required, but purely manual extraction is infeasible. We present a framework and an accompanying tool for information extraction using weak-supervision labelling with human validation. We demonstrate our approach on three criminal justice datasets. We find that the combination of computer speed and human understanding yields precision comparable to manual annotation while requiring only a fraction of time, and significantly outperforms fully automated baselines in terms of precision.

We test the hypothesis that language models trained with reinforcement learning from human feedback (RLHF) have the capability to "morally self-correct" -- to avoid producing harmful outputs -- if instructed to do so. We find strong evidence in support of this hypothesis across three different experiments, each of which reveal different facets of moral self-correction. We find that the capability for moral self-correction emerges at 22B model parameters, and typically improves with increasing model size and RLHF training. We believe that at this level of scale, language models obtain two capabilities that they can use for moral self-correction: (1) they can follow instructions and (2) they can learn complex normative concepts of harm like stereotyping, bias, and discrimination. As such, they can follow instructions to avoid certain kinds of morally harmful outputs. We believe our results are cause for cautious optimism regarding the ability to train language models to abide by ethical principles.

The growing use of automated decision-making in critical applications, such as crime prediction and college admission, has raised questions about fairness in machine learning. How can we decide whether different treatments are reasonable or discriminatory? In this paper, we investigate discrimination in machine learning from a visual analytics perspective and propose an interactive visualization tool, DiscriLens, to support a more comprehensive analysis. To reveal detailed information on algorithmic discrimination, DiscriLens identifies a collection of potentially discriminatory itemsets based on causal modeling and classification rules mining. By combining an extended Euler diagram with a matrix-based visualization, we develop a novel set visualization to facilitate the exploration and interpretation of discriminatory itemsets. A user study shows that users can interpret the visually encoded information in DiscriLens quickly and accurately. Use cases demonstrate that DiscriLens provides informative guidance in understanding and reducing algorithmic discrimination.