We interviewed Karine Perset, from the OECD Directorate for Science, Technology and Innovation in France about the informational pillars that make up strong AI governance for governments worldwide. She offered us numerous insights into how the OECD developed the AI Principles and works with governing bodies to design policies that will keep AI safe and trustworthy into the future. Additionally, we discuss which types of policies are necessary for which types of AI software as well as the scale at which these policies should govern. This could range from city to city at the local level as well as an international level as governments around the world continue to work on global standards for AI governance. In our interview with Perset, we focused on the differences between local, international, and global policies for AI systems and products.

Recent times are witnessing rapid development in machine learning algorithm systems, especially in reinforcement learning, natural language processing, computer and robot vision, image processing, speech, and emotional processing and understanding. In tune with the increasing importance and relevance of machine learning models, algorithms, and their applications, and with the emergence of more innovative uses cases of deep learning and artificial intelligence, the current volume presents a few innovative research works and their applications in real world, such as stock trading, medical and healthcare systems, and software automation. The chapters in the book illustrate how machine learning and deep learning algorithms and models are designed, optimized, and deployed. The volume will be useful for advanced graduate and doctoral students, researchers, faculty members of universities, practicing data scientists and data engineers, professionals, and consultants working on the broad areas of machine learning, deep learning, and artificial intelligence.

We develop the "generalized consistent weighted sampling" (GCWS) for hashing the "powered-GMM" (pGMM) kernel (with a tuning parameter $p$). It turns out that GCWS provides a numerically stable scheme for applying power transformation on the original data, regardless of the magnitude of $p$ and the data. The power transformation is often effective for boosting the performance, in many cases considerably so. We feed the hashed data to neural networks on a variety of public classification datasets and name our method ``GCWSNet''. Our extensive experiments show that GCWSNet often improves the classification accuracy. Furthermore, it is evident from the experiments that GCWSNet converges substantially faster. In fact, GCWS often reaches a reasonable accuracy with merely (less than) one epoch of the training process. This property is much desired because many applications, such as advertisement click-through rate (CTR) prediction models, or data streams (i.e., data seen only once), often train just one epoch. Another beneficial side effect is that the computations of the first layer of the neural networks become additions instead of multiplications because the input data become binary (and highly sparse). Empirical comparisons with (normalized) random Fourier features (NRFF) are provided. We also propose to reduce the model size of GCWSNet by count-sketch and develop the theory for analyzing the impact of using count-sketch on the accuracy of GCWS. Our analysis shows that an ``8-bit'' strategy should work well in that we can always apply an 8-bit count-sketch hashing on the output of GCWS hashing without hurting the accuracy much. There are many other ways to take advantage of GCWS when training deep neural networks. For example, one can apply GCWS on the outputs of the last layer to boost the accuracy of trained deep neural networks.

According to a new survey, six out of ten expect that products and services using artificial intelligence will profoundly change their daily life in the next three to five years and half feel that this has already happened. These are some of the findings of a 28-country survey conducted by Ipsos for the World Economic Forum of 19,504 adults under the age of 75 between November 19 and December 3, 2021. "In order to trust artificial intelligence, people must know and understand exactly what AI is, what it's doing, and its impact," said Kay Firth-Butterfield, Head of Artificial Intelligence and Machine Learning at the World Economic Forum. "Leaders and companies must make transparent and trustworthy AI a priority as they implement this technology. At the World Economic Forum, we are focused on multi-stakeholder collaboration to optimize accountability, transparency, privacy and impartiality to create that trust. With the ability to solve many of society's pressing issues, we are focused on accelerating the benefits and mitigating the risks of artificial intelligence and machine learning. Only then can we gain public trust and benefit from the rewards of emerging tech like AI."

In 1970, Marvin Minsky, recipient of the Turing Award ("the Nobel Prize of Computing"), predicted that within "three to eight years we will have a machine with the general intelligence of an average human being." The fundamental roadblock is that, although computer algorithms are really, really good at identifying statistical patterns, they have no way of knowing what these patterns mean because they are confined to MathWorld and never experience the real world. It's a brown-throated thrush, but in Germany it's called a halsenflugel, and in Chinese they call it a chung ling and even if you know all those names for it, you still know nothing about the bird–you only know something about people; what they call that bird. Now that thrush sings, and teaches its young to fly, and flies so many miles away during the summer across the country, and nobody knows how it finds its way," and so forth. There is a difference between the name of the thing and what goes on.

TPG's involvement in Fractal includes a mix of primary investment and a secondary share purchase from funds advised by buyout firm and existing investor Apax Partners. The share purchase deal will close by the first quarter of this year, following which Apax will continue to remain a major shareholder of the startup. Co-founded by Srikanth Velamakanni and Pranay Agrawal in Mumbai in 2000, New York-headquartered Fractal provides artificial intelligence and analytics solutions to Fortune 500 companies. "We continue to see great momentum in how clients are leveraging AI to accelerate digital transformation," Velamakanni said in a statement. The company's products include Qure.ai that helps radiologists make diagnostic decisions, and Theremin.ai,

Men who only briefly catch a glimpse of a woman are much more likely to overestimate how attractive she is than a woman glimpsing a man, a study reveals. Researchers, led by Murdoch University, in Perth Australia, worked with nearly 400 volunteers, asking them to rate the attractiveness of people of the opposite-sex from a blurry image, and then from a clear image. The results showed that on average men overestimate women's attractiveness, whereas on average women underestimate men's attractiveness. If you've been having trouble finding love on dating apps, you might want to try dating one of your friends. A study looked at data from just under 2,000 couples of different demographics in Canada.

Jan 5 (Reuters) - Artificial intelligence technology startup Fractal said on Wednesday it has received a $360 million investment from private equity firm TPG through its Asia focused investment platform. TPG's involvement in Fractal includes a mix of primary investment and a secondary share purchase from funds advised by buyout firm and existing investor Apax Partners. The share purchase deal will close by the first quarter of this year, following which Apax will continue to remain a major shareholder of the startup. Co-founded by Srikanth Velamakanni and Pranay Agrawal in Mumbai in 2000, New York-headquartered Fractal provides artificial intelligence and analytics solutions to Fortune 500 companies. "We continue to see great momentum in how clients are leveraging AI to accelerate digital transformation," Velamakanni said in a statement.

We perform a systematic analysis of the quality of fit of the stochastic block model (SBM) for 275 empirical networks spanning a wide range of domains and orders of size magnitude. We employ posterior predictive model checking as a criterion to assess the quality of fit, which involves comparing networks generated by the inferred model with the empirical network, according to a set of network descriptors. We observe that the SBM is capable of providing an accurate description for the majority of networks considered, but falls short of saturating all modeling requirements. In particular, networks possessing a large diameter and slow-mixing random walks tend to be badly described by the SBM. However, contrary to what is often assumed, networks with a high abundance of triangles can be well described by the SBM in many cases. We demonstrate that simple network descriptors can be used to evaluate whether or not the SBM can provide a sufficiently accurate representation, potentially pointing to possible model extensions that can systematically improve the expressiveness of this class of models.

Frame shift is a cross-linguistic phenomenon in translation which results in corresponding pairs of linguistic material evoking different frames. The ability to predict frame shifts enables automatic creation of multilingual FrameNets through annotation projection. Here, we propose the Frame Shift Prediction task and demonstrate that graph attention networks, combined with auxiliary training, can learn cross-linguistic frame-to-frame correspondence and predict frame shifts.