New York Gov. Andrew Cuomo signed legislation in late July to create a temporary state commission that will examine how artificial intelligence impacts his state. In doing so, New York joined Vermont, Alabama, and Washington in establishing an A.I. task force that will examine the cutting-edge technology and then make recommendations about how it should be regulated. The groups vary in their mission, but the general message is the same: companies pushing A.I., the brains behind innovation like robotics and facial recognition software, can't necessarily be trusted to do what's in the best interest of state residents. Brandie Nonnecke, founding director of University of California's Center for Information Technology Research in the Interest of Society Policy Lab, says that task forces could help keep state lawmakers up to date about the technology. The end result, she says, will be better-written bills that don't get stuck in legislative purgatory.

At the China Online Literature conference last week, Chinese search engine Sogou announced plans to create artificial intelligence-powered avatars that look and sound like two of the country's most famous authors--taking the experience of listening to an audiobook to an entirely new level. The first authors to get the A.I. avatar treatment will be Yue Guan and Bu Xin Tian Shang Diao Xian Bing. But if the project is successful, it could be a jumping off point for the industry to create avatars of even more authors.

Have you ever thought about the craze hovering around artificial intelligence? No doubt, with its extreme capabilities, it is shining brightly in the software industry. Most importantly, among companies that are involved in manufacturing, transport, services, and the list goes on. It isn't surprising to see companies capitalizing on the applications of artificial intelligence in making our day to day activities easy. Over the past few years, there has been an abundance of stories related to the success of artificial intelligence.

The concept of someone using artificial intelligence to identify a criminal is no longer just a science fiction plotline. Developments in AI, as well as facial recognition technology, has opened a pandora's box to the coming age of AI policing, something that is equally exciting and terrifying. Recently a man accused of taking the life of his girlfriend in Southeast China was caught all thanks to an AI-driven facial recognition program. Crime-fighting AI is a commonly used trope in both film, television, and anime. Minority Report, Person of Interest, and the critically acclaimed anime Psycho-Pass have plots that center around an advanced technological system ( which is usually AI) that can identify potential criminals.

Machine learning (ML) has made it possible for technologists to do amazing things with data. Its arrival coincides with the evolution of networked manufacturing systems driven by IoT, also known as industrial IoT (IIoT), which has led to an exponential growth in the data available for statistical modeling. Predictive maintenance (PdM) applications aim to apply ML on IIoT datasets in order to reduce occupational hazards, machine downtime, and other costs by detecting when machines exhibit characteristics associated with past failures. Implementing PdM involves a process that starts with data preparation and ends with applied ML. Practitioners know well that the bulk of the effort required for effective ML deals with data preparation, and yet, those challenges continue to be underrepresented in ML literature, where authors tend to demonstrate concepts with contrived datasets. Connect, manage, and observe microservices-based applications with security-focused Istio and Red Hat OpenShift.

As Machine Learning (ML) is becoming an important part of every industry, the demand for Machine Learning Engineers (MLE) has grown dramatically. MLEs combine machine learning skills with software engineering knowhow to find high-performing models for a given application and handle the implementation challenges that come up -- from building out training infrastructure to preparing models for deployment. New online resources have sprouted in parallel to train engineers to build ML models and solve the various software challenges encountered. However, one of the most common hurdles with new ML teams is maintaining the same level of forward progress that engineers are accustomed to with traditional software engineering. The most pressing reason for this challenge is that the process of developing new ML models is highly uncertain at the outset.

Want to get a jump-start on upcoming deals? If a lack of innovation is to blame for stagnating U.S. construction productivity over the last half-century, off-site builders -- and some of their AI partners -- think they may have the solution. Around the country, prefabrication companies of different scopes and specializations are trying to boost their factory environments by using artificial intelligence. Off-site construction firms promise both time and cost savings, with one June report by McKinsey predicting up to 50% quicker builds compared to on-site construction. Cost savings from off-site construction, on the other hand, only now appear to be materializing for builders, in part because of the use of AI.

Artificial Intelligence (AI) systems are becoming smarter every day, beating world champions in games like Go, identifying tumours in medical scans better than human radiologists, and increasing the efficiency of electricity-hungry data centres. Some economists are comparing the transformative potential of AI with other "general purpose technologies" such as the steam engine, electricity or the transistor. But current AI systems are far from perfect. They tend to reflect the biases of the data used to train them and to break down when they face unexpected situations. They can be gamed, as we have seen with the controversies surrounding misinformation on social media, violent content posted on YouTube, or the famous case of Tay, the Microsoft chatbot, which was manipulated into making racist and sexist statements within hours.

Chinese artificial intelligence provider Megvii Technology Ltd, backed by Alibaba, has filed for an IPO of at least $500 million in Hong Kong, two people said, coming at a time of political unrest in the city and potential first recession in a decade. Megvii, widely known for its facial recognition platform Face, may raise as much as $1 billion in the planned initial public offering, said one of the people, who expected the deal to take place in the fourth quarter of the year. The Beijing-based firm is moving ahead with its planned float while some other companies have postponed or slowed down their listing plans due to the lack of financial and political stability in Hong Kong. Reuters reported last week that China's biggest e-commerce company Alibaba Group Holding Ltd, also a big shareholder of Megvii, had delayed its up to $15 billion listing in Hong Kong.

Perhaps the most frequent objection to the development of artificial intelligence is the lack of certainty over whether such a powerful innovation will genuinely be good for humanity. Who is responsible if an AI-powered device harms someone? How will we ever know whether AI can behave morally? Where should AI be put to use, and where is it better off left out? These are fundamental questions that yield many different answers from experts in the field of Artificial Intelligence Ethics. This field, which has exploded because of the rapid growth of artificial intelligence, explores the philosophical issues posed by this new technology.