Bayesian predictive coding is a putative neuromorphic method for acquiring higher-level neural representations to account for sensory input. Although originating in the neuroscience community, there are also efforts in the machine learning community to study these models. This paper reviews some of the more well known models. Our review analyzes module connectivity and patterns of information transfer, seeking to find general principles used across the models. We also survey some recent attempts to cast these models within a deep learning framework. A defining feature of Bayesian predictive coding is that it uses top-down, reconstructive mechanisms to predict incoming sensory inputs or their lower-level representations. Discrepancies between the predicted and the actual inputs, known as prediction errors, then give rise to future learning that refines and improves the predictive accuracy of learned higher-level representations. Predictive coding models intended to describe computations in the neocortex emerged prior to the development of deep learning and used a communication structure between modules that we name the Rao-Ballard protocol. This protocol was derived from a Bayesian generative model with some rather strong statistical assumptions. The RB protocol provides a rubric to assess the fidelity of deep learning models that claim to implement predictive coding.

ReverseAds announced the launch of its reverse-engineered search advertising solution that uses Big Data, A.I., and predictive modeling to help brands serve intuitive ads everywhere buyers go online after their initial search. ReverseAds addresses the need for predictive multi-channel ad campaigns that provide total visibility of the buyer's journey, allowing brands to move beyond underperforming search ads. This approach to digital advertising prioritizes ROI and CPA compared to the CPC bidding model provided by Google. With ReverseAds, considered purchase brands gain access to unprecedented amounts of intent data and a USPTO provisional patent-approved Assignment Algorithm. The algorithm uses predictive learning A.I. to determine which keywords will drive a business's highest total conversion.

America's most famous spy agency has a major competitor it can't quite seem to beat: Silicon Valley. The CIA has long been a place cutting-edge technology is researched, developed, and realized--and it wants to lead in fields like artificial intelligence and biotechnology. However, recruiting and retaining the talent capable of building these tools is a challenge on many levels, especially since a spy agency can't match Silicon Valley salaries, reputations, and patents. The agency's solution is CIA Labs, a new skunkworks that will attempt to recruit and retain technical talent by offering incentives to those who work there. Under the new initiative, announced today, CIA officers will be able for the first time to publicly file patents on the intellectual property they work on--and collect a portion of the the profits.

By Ratna Mehta Technological advancement is a double-edged sword; while it oils the wheels of advancement and innovation leading to breakthroughs that improve efficiency, rationalise cost and improve the quality of life, it has its fallouts, i.e. job losses, health issues and environmental pollution. Man vs Machine With the rise of AI, there is increasing anxiety around massive job displacement. This is substantiated by widespread research: - Accountants have a 95% chance of losing jobs - 29% of legal sector jobs could be automated in 10 years - Intelligent agents and robots could replace 30% of the world's current human labour Being a trader was an esteemed profession, but with AI systems that can analyse information from markets, social media, corporate filings and economic conditions to quickly decipher trades, these systems can trade better than any human. As per analysis firm Oxford Economics, up to 20 million manufacturing jobs around the world could be replaced with robots by 2030. Man and Machine Joining Forces How we use technology depends on our perspective; we can use it to'replace' humans or we can leverage it to'augment' humans.

Algorithms have taken a lot of flak recently, particularly those being used by the government and other public bodies in the UK. The controversial algorithm used to award student grades caused a huge public outcry, but national and local governments and several police forces have been withdrawing other algorithms and artificial intelligence tools from use throughout the year in response to legal challenges and design failures. This has quite rightly brought it home to public sector organisations that a more critical approach to AI and algorithmic decision-making is needed. But there are many cases in which government bodies can deploy such technology in lower risk, high-impact scenarios that can improve lives, particularly if they don't directly use personal data. So before we leap full pelt into AI cynicism we should consider benefits as well as risks it offers, and demand a more responsible approach to AI development and deployment.

We propose a new test to measure a text model's multitask accuracy. The test covers 57 tasks including elementary mathematics, US history, computer science, law, and more. To attain high accuracy on this test, models must possess extensive world knowledge and problem solving ability. We find that while most recent models have near random-chance accuracy, the very largest GPT-3 model improves over random chance by almost 20 percentage points on average. However, on every one of the 57 tasks, the best models still need substantial improvements before they can reach expert-level accuracy. Models also have lopsided performance and frequently do not know when they are wrong. Worse, they still have near-random accuracy on some socially important subjects such as morality and law. By comprehensively evaluating the breadth and depth of a model's academic and professional understanding, our test can be used to analyze models across many tasks and to identify important shortcomings.

In this paper we suggest a minimally-supervised approach for identifying nuanced frames in news article coverage of politically divisive topics. We suggest to break the broad policy frames suggested by Boydstun et al., 2014 into fine-grained subframes which can capture differences in political ideology in a better way. We evaluate the suggested subframes and their embedding, learned using minimal supervision, over three topics, namely, immigration, gun-control and abortion. We demonstrate the ability of the subframes to capture ideological differences and analyze political discourse in news media.

Autonomous Intelligent Systems are designed to reduce the need for human intervention in our daily life. However, the full benefit of these new systems will be attained only if they are aligned with society's values and ethical principles. Adopting ethical approaches to building such systems has been attracting a lot of attention in the recent years. The global concern about the ethical behavior of this kind of technologies has manifested in many initiatives at different levels. As examples, we mention: the IEEE initiative for ethically aligned design of autonomous intelligent systems ('Ethics in Action'

The number of blockchain users has tremendously grown in recent years. As an unintended consequence, e-crime transactions on blockchains has been on the rise. Consequently, public blockchains have become a hotbed of research for developing AI tools to detect and trace users and transactions that are related to e-crime. We argue that following a few select strategies can make money laundering on blockchain virtually undetectable with most of the existing tools and algorithms. As a result, the effective combating of e-crime activities involving cryptocurrencies requires the development of novel analytic methodology in AI.

The connections between logic programming and law have been studied for a long time. In 1975, Meldman discussed his PhD Thesis entitled "A preliminary study in computer-aided legal analysis" [12] where he modelled legal facts in a Lisp-like language and used instantiation (recalling unification) and syllogism (recalling resolution) to perform a simple kind of legal analysis inspired by Prosser's Law of Torts [13]. At that time Prolog was just born, but its applications to legal reasoning were not long in coming. One of the first attempts was made by Hustler [9] who implemented a prototype of a legal consultant in Prolog, again inspired by Prosser's work. A few years later, Kowalski, Sergot et al. succeeded in running a significant portion of the 1981 British Nationality Act, implemented in Prolog on a small micro computer [15]. In the same years, Prolog became very popular for implementing expert systems for the legal domain [3, 19]. From those early attempts, much progress has been made: research on deontic and defeasible reasoning [1, 5], ontological reasoning [7], and argumentation [8, 18] is extremely lively and helps disclosing the many connections between logic programming (and, more in general, computational logic and automated reasoning) and legal reasoning. The application of automated reasoning to digital forensics is another promising research direction [6] whose potential is witnessed by the ongoing "Digital Forensics: Evidence Analysis via Intelligent Systems and Practices" (DigForASP) COST Action