So read the T-shirt sported by Ben Recht, a professor at the University of California, Berkeley, as he collected an award at the Neural Information Processing Systems (NIPS) conference this week. Dr Recht, pictured above in lecture mode, was protesting against the flood of corporate money pouring into NIPS, aping the words Kurt Cobain wrote on a T-shirt when he appeared on the cover of Rolling Stone in 1992. "It's not an academic conference anymore," Dr Recht says wistfully, perched in the Californian sun on the steps of the Long Beach Convention Centre. He complains that folk would rather go to corporate-sponsored parties these days (Intel's featured Flo Rida, a rapper), than poster sessions. AI, it seems, is the new rock and roll.

The U.S. Department of Defense on Feb. 12 released its roadmap for artificial intelligence, and the most interesting thing about it might be what's missing from the report: The military is nowhere close to building a lethal weapon capable of thinking and acting on its own. As it turns out, the military applications of artificial intelligence today and in the foreseeable future are much more mundane. The Defense Department has several pilot projects in the works that focus on using AI to solve everyday problems such as floods, fires, and maintenance, said U.S. Air Force Lt. Gen. Jack Shanahan, who heads up the Pentagon's new Joint Artificial Intelligence Center. "We are nowhere close to the full autonomy question that most people seem to leap to a conclusion on when they think about DoD and AI," Shanahan said during a briefing Tuesday. It's not that Department of Defense hasn't given the idea of fully autonomous weapons much thought.

The expanding use of AI is attracting new attention to the importance of workforce diversity. Although tech companies have stepped up efforts to recruit women and minorities, computer and software professionals who write AI programs are still largely white and male, Bureau of Labor Statistics data show. Developers testing their products often rely on data sets that lack adequate representation of women or minority groups. One widely used data set is more than 74% male and 83% white, research shows. Thus, when engineers test algorithms on these databases with high numbers of people like themselves, they may work fine.

Ecosia, a German startup with an internet search engine, today, has brought in enough revenues to enable it to plant 50 million trees. This equates to the removal of 2.5 million tonnes of Co2 from the atmosphere, according to the company. Ecosia has used the profits from advertisements on its search engine to plant trees in Kenya, Brazil, Indonesia, Spain, Tanzania, Madagascar, Colombia, Peru, Senegal, Burkina Faso, Haiti, Morocco, Ethiopia, Uganda, Ghana and Nicaragua. Ecosia has partnered with Bing, Microsoft's search engine, to get results for users, but receives a majority portion of any revenues. After covering its internal costs, everything left goes towards planting trees; Ecosia is a non-profit organization.

A new WIPO flagship study has documented a massive recent surge in artificial intelligence-based inventions, with U.S.-based companies IBM and Microsoft leading the pack as AI has moved from the theoretical realm toward the global marketplace in recent years. The first publication in the "WIPO Technology Trends" series defines and measures innovations in artificial intelligence (AI), uncovering more than 340,000 AI-related patent applications and 1.6 million scientific papers published since AI first emerged in the 1950s, with the majority of all AI-related patent filings published since 2013. This inaugural Technology Trends report provides a common information base on AI for policy and decision makers in government and business, as well as concerned citizens across the globe, who are grappling with the ramifications of a new technology that promises to upend many areas of economic, social and cultural activity. "Patenting activity in the artificial intelligence realm is rising at a rapid pace, meaning we can expect a very significant number of new AI-based products, applications and techniques that will alter our daily lives – and also shape future human interaction with the machines we created," said WIPO Director General Francis Gurry. "AI's ramifications for the future of human development are profound. The first step in maximizing the widespread benefit of AI, while addressing ethical, legal and regulatory challenges, is to create a common factual basis for understanding of artificial intelligence. In unveiling the first in our "WIPO Technology Trends" series, WIPO is pleased to contribute evidence-based projections, thereby informing global policymaking on the future of AI, its governance and the IP framework that supports it," said Mr. Gurry.

Man's closest animal relative, chimps, communicate in a distinctly'human-like' way, scientists have found. The primates use gestures that follow some of the same rules as basic human language. One was Zipf's law of abbreviation, which says commonly used words tend to be shorter, and the other is Menzerath's law, which predicts that larger linguistic structures are made up of shorter parts - such as syllables within spoken words. Experts made the discovery after studying videos of wild chimps living in Uganda's Budongo Forest Reserve. Chimpanzee sign language apes the way humans communicate, research has shown.

Learning image representations to capture fine-grained semantics has been a challenging and important task enabling many applications such as image search and clustering. In this paper, we present Graph-Regularized Image Semantic Embedding (Graph-RISE), a large-scale neural graph learning framework that allows us to train embeddings to discriminate an unprecedented O(40M) ultra-fine-grained semantic labels. Graph-RISE outperforms state-of-the-art image embedding algorithms on several evaluation tasks, including image classification and triplet ranking. We provide case studies to demonstrate that, qualitatively, image retrieval based on Graph-RISE effectively captures semantics and, compared to the state-of-the-art, differentiates nuances at levels that are closer to human-perception.

Rational decision making in its linguistic description means making logical decisions. In essence, a rational agent optimally processes all relevant information to achieve its goal. Rationality has two elements and these are the use of relevant information and the efficient processing of such information. In reality, relevant information is incomplete, imperfect and the processing engine, which is a brain for humans, is suboptimal. Humans are risk averse rather than utility maximizers. In the real world, problems are predominantly non-convex and this makes the idea of rational decision-making fundamentally unachievable and Herbert Simon called this bounded rationality. There is a trade-off between the amount of information used for decision-making and the complexity of the decision model used. This explores whether machine rationality is subjective and concludes that indeed it is.

This paper considers the perturbed stochastic gradient descent algorithm and shows that it finds $\epsilon$-second order stationary points ($\left\|\nabla f(x)\right\|\leq \epsilon$ and $\nabla^2 f(x) \succeq -\sqrt{\epsilon} \mathbf{I}$) in $\tilde{O}(d/\epsilon^4)$ iterations, giving the first result that has linear dependence on dimension for this setting. For the special case, where stochastic gradients are Lipschitz, the dependence on dimension reduces to polylogarithmic. In addition to giving new results, this paper also presents a simplified proof strategy that gives a shorter and more elegant proof of previously known results (Jin et al. 2017) on perturbed gradient descent algorithm.

Artificial Intelligence (AI) is necessary to address the large and growing deficit in retina and healthcare access globally. And mobile AI diagnostic platforms running in the Cloud may effectively and efficiently distribute such AI capability. Here we sought to evaluate the feasibility of Cloud-based mobile artificial intelligence for detection of retinal disease. And to evaluate the accuracy of a particular such system for detection of subretinal fluid (SRF) and macula edema (ME) on OCT scans. A multicenter retrospective image analysis was conducted in which board-certified ophthalmologists with fellowship training in retina evaluated OCT images of the macula. They noted the presence or absence of ME or SRF, then compared their assessment to that obtained from Fluid Intelligence, a mobile AI app that detects SRF and ME on OCT scans. Investigators consecutively selected retinal OCTs, while making effort to balance the number of scans with retinal fluid and scans without. Exclusion criteria included poor scan quality, ambiguous features, macula holes, retinoschisis, and dense epiretinal membranes. Accuracy in the form of sensitivity and specificity of the AI mobile App was determined by comparing its assessments to those of the retina specialists. At the time of this submission, five centers have completed their initial studies. This consists of a total of 283 OCT scans of which 155 had either ME or SRF ("wet") and 128 did not ("dry"). The sensitivity ranged from 82.5% to 97% with a weighted average of 89.3%. The specificity ranged from 52% to 100% with a weighted average of 81.23%. CONCLUSION: Cloud-based Mobile AI technology is feasible for the detection retinal disease. In particular, Fluid Intelligence (alpha version), is sufficiently accurate as a screening tool for SRF and ME, especially in underserved areas. Further studies and technology development is needed.