The past year has been rich in events, discoveries and developments in AI. It is hard to sort through the noise to see if the signal is there and, if it is, what is the signal saying. This post attempts to get you exactly that: I'll try to extract some of the patterns in the AI landscape over the past year. And, if we are lucky, we'll see how some of the trends extend into the near future. Make no mistake: this is an opinion piece. I am not trying to establish some comprehensive record of accomplishments for the year. I am merely trying to outline some of these trends. Another caveat: this review is US-centric. A lot of interesting things are happening, say, in China, but I, unfortunately, am not familiar with that exciting ecosystem.

Microsoft is spending heavily on its artificial-intelligence tech. But it wants investors to know that the tech may go awry, harming the company's reputation in the process. Or so it warned investors in its latest quarterly report, as first spotted by Quartz's Dave Gershgorn. "Issues in the use of AI in our offerings may result in reputational harm or liability," Microsoft wrote in the filing. "AI algorithms may be flawed. Datasets may be insufficient or contain biased information. Inappropriate or controversial data practices by Microsoft or others could impair the acceptance of AI solutions. You can read the full filing below. Despite the big talk from tech companies such as Microsoft about the virtues and possibilities of AI, the truth is that the technology is not that smart yet. Today, AI is mostly based on machine learning, in which the computer has a limited ability to infer conclusions from limited data. It must ingest many examples for it to "understand" something, and if that initial data set is biased or flawed, its output will be, too. Intel and startups such as Habana Labs are working on chips that could help computers better perform the complicated task of inference. Inference is the foundation of learning and the ability of humans (and machines) to reason. And Microsoft has already had a few high-profile cases of snafus with its AI tech. In 2016, Microsoft yanked a Twitter chatbot called Tay offline within 24 hours after it began spewing racist and sexist tweets, using words taught to it by trolls. More recently, and more seriously, was research done by Joy Buolamwini at the MIT Media Lab, reported on a year ago by The New York Times. She found three leading facial-recognition systems -- created by Microsoft, IBM, and China's Megvii -- were doing a terrible job identifying nonwhite faces. Microsoft's error rate for darker-skinned women was 21%, which was still better compared with 35% for the other two. Microsoft insists that it listened to that criticism and has improved its facial-recognition technology. Plus, in the wake of outcry over Amazon's Rekognition facial-recognition service, Microsoft has begun calling for regulation of facial-recognition tech. Microsoft CEO Satya Nadella told journalists last month: "Take this notion of facial recognition, right now it's just terrible.

The artificial intelligence (AI) sector plays a significant part in resolving some of the most critical difficulties encountered by organisations as well as consumers. The extensive adoption of AI and cognitive processes across all industries will accelerate worldwide earnings for the AI sector from approximately USD 16.06 billion in 2017 to a whopping USD 190.61 billion by 2025! Therefore, enrolling yourself in artificial intelligence courses is an obvious move if you wish to land a satisfactory job in the ever-growing AI field. With the tremendous technological advancement in AI on a global scale, India is one country that is surpassing several other countries in different AI domains. Here are the top seven Indian Startups which are pushing the boundaries in the AI sector in 2018.

In 1970, Lyudmila Terentyevna Aleksandrova lost her right hand. It happened at work, where she was employed by the Russian state. With her hand gone, she fought for a disability allowance that never materialized, batted about by district and regional courts. Eventually, after decades of frustration, she brought the case to the European Court of Human Rights, which ruled in 2007 that there had been a violation in Aleksandrova's right to a fair trial. Pay the money, it told Russia.

Human extinction may be the stuff of nightmares but there are many ways in which it could happen. Popular culture tends to focus on only the most spectacular possibilities: think of the hurtling asteroid of the film Armageddon or the alien invasion of Independence Day. While a dramatic end to humanity is possible, focusing on such scenarios may mean ignoring the most serious threats we face in today's world. And it could be that we are able to do something about these. In 1815 an eruption of Mount Tambora, in Indonesia, killed more than 70,000 people, while hurling volcanic ash into the upper atmosphere.

Artificial intelligence is advancing rapidly. It is powering autonomous vehicles and being applied in areas from health care and finance to retail sales and national defense. As noted in a 2018 Brookings Institution report, "AI is a technology that is transforming every walk of life. It is a wide-ranging tool that enables people to rethink how we integrate information, analyze data, and use the resulting insights to improve decisionmaking." Yet most of the current AI impetus in the United States comes from the private sector.

Having now worked in the mobile apps industry for several years, I'm troubled that the most popular technology trends at CES will harm the developmental health of our children. Even worse, I feel that technology companies, government, and society are likely to make the same mistakes made with mobile screen time -- though at a much greater cost. With an urgency similar to what has moved nations to ward off the worst of global warming, we too must act now to prepare our children and better understand and regulate organizations developing artificial intelligence (AI), virtual reality (VR), and 5G (5th generation mobile connectivity). This is why I'm asking the United States federal government to appoint a task force consisting of consumer advocates and technology experts to recommend policy to the relevant Congressional committees and the Federal Trade Commission. Even though the emerging technologies at CES are rolling out very quickly, there were plenty of headlines in 2018 suggesting that the crises with mobile screen time is far from fully understand, let alone resolved.

This is the question many Hollywood blockbusters have been trying to answer for decades. The story of man versus machine has captivated audiences across generations for one simple reason - it plays to our underlying fears of becoming obsolete. During the industrial revolution at the turn of the last century, there were fears automation via assembly lines and other technology would lead to mass unemployment, and it makes sense that the new wave of industrial revolution, which centers on artificial intelligence, may eliminate jobs and render humans obsolete. The good news is that AI will create jobs even as it eliminates other jobs, so the best way to prepare yourself is to learn a new set of skills. Which jobs will be at risk for automation?

This paper shows that the fuzzy temporal logic can model figures of thought to describe decision-making behaviors. In order to exemplify, some economic behaviors observed experimentally were modeled from problems of choice containing time, uncertainty and fuzziness. Related to time preference, it is noted that the subadditive discounting is mandatory in positive rewards situations and, consequently, results in the magnitude effect and time effect, where the last has a stronger discounting for earlier delay periods (as in, one hour, one day), but a weaker discounting for longer delay periods (for instance, six months, one year, ten years). In addition, it is possible to explain the preference reversal (change of preference when two rewards proposed on different dates are shifted in the time). Related to the Prospect Theory, it is shown that the risk seeking and the risk aversion are magnitude dependents, where the risk seeking may disappear when the values to be lost are very high.

A human brain is capable of continual learning by nature; however the current mainstream deep neural networks suffer from a phenomenon named catastrophic forgetting (i.e., learning a new set of patterns suddenly and completely would result in fully forgetting what has already been learned). In this paper we propose a generic learning model, which regards a learning system as a fiber bundle. By comparing the learning performance of our model with conventional ones whose neural networks are multilayer perceptrons through a variety of machine-learning experiments, we found our proposed model not only enjoys a distinguished capability of continual learning but also bears a high information capacity. In addition, we found in some learning scenarios the learning performance can be further enhanced by making the learning time-aware to mimic the episodic memory in human brain. Last but not least, we found that the properties of forgetting in our model correspond well to those of human memory. This work may shed light on how a human brain learns.