Companies are besieged by information and bedazzled by IT solutions. With the rapid advancements in information technology, high-speed Internet, mobile technology and artificial intelligence, we now have access to huge amounts of data about customers, their demographics, and their online behavior across all touch points. The advantage of access to so much information is not just increased revenue and long-lasting customer relationships, but also the ability to develop sensitivity to warning signals, which allow companies to prevent or mitigate disasters. The avoidance of conflict, the management of cyclical downturns or strategic missteps, and the management of the company's future are at the core of creating intelligent businesses. Companies have improved their practices with respect to capturing greater amounts of data.

The contracting process is about taking smart risks and inspiring trust. Those factors will only grow more important as legal organizations embrace digital transformation. Artificial Intelligence (AI) solutions that get smarter over time, powered by massive amounts of data, can help lawyers earn that credibility and use information more thoughtfully and quickly. Lawyers using tools for contract analytics can embrace the "growth mindset" and better leverage their past experiences from prior deals and transactions to help their clients take smart risks in subsequent matters. Doing so will also help break down silos as legal and business teams can internally share data and lessons learned.

Me: "Alexa, tell me what will happen in 2019." Amazon AI: "Do you want to open'this day in history'?" Me: "Alexa, give me a prediction for 2019." Amazon AI: "The crystal ball is clouded, I can't tell." My conversation with Amazon's "smart speaker" or "intelligent voice assistant" just about sums up the present state of "artificial intelligence" (AI) at home, the office, and the factory: Try a few times and sooner or later you will probably get the correct action the human intelligence behind it programmed it to perform. What will be the state of AI in 2019? The following list features 120 senior executives involved with AI, all peering into their not-so-clouded crystal ball, and promising less hype and more practical, precise, and narrow AI. "Self-Driving Finance is a practical implementation of AI that is already used in one form or another by millions of bank customers around the globe and will only get better in the coming years. Based on projects that are currently underway with ...

Me: "Alexa, tell me what will happen in 2019." Amazon AI: "Do you want to open'this day in history'?" Me: "Alexa, give me a prediction for 2019." Amazon AI: "The crystal ball is clouded, I can't tell." My conversation with Amazon's "smart speaker" or "intelligent voice assistant" just about sums up the present state of "artificial intelligence" (AI) at home, the office, and the factory: Try a few times and sooner or later you will probably get the correct action the human intelligence behind it programmed it to perform. What will be the state of AI in 2019? The following list features 120 senior executives involved with AI, all peering into their not-so-clouded crystal ball, and promising less hype and more practical, precise, and narrow AI. "Self-Driving Finance is a practical implementation of AI that is already used in one form or another by millions of bank customers around the globe and will only get better in the coming years. Based on projects that are currently underway with ...

Okay, that may be the most complicated title that I've ever read, but here's the point of that title: Most people don't know what they already know. Maybe spun another way, is that people don't realize how much they really know, that much of what they really know is buried behind general rules, rules of thumbs and simple heuristics. But those heuristics get in the way of one's real knowledge. And that's the real secret to effective design thinking – helping subject matter experts to uncover what they don't realize that they already know. My Saturday morning coffee conversations with John Morley always surface new ways to think about how to leverage design to help "accelerate the commercialization and monetization of intellectual property (IP) assets".

[T]he country does not appear to fully understand the risks involved in enhancing that welfare state through artificial intelligence applications. The municipality of Gladsaxe in Copenhagen, for example, has quietly been experimenting with a system that would use algorithms to identify children at risk of abuse, allowing authorities to target the flagged families for early intervention that could ultimately result in forced removals. As deep learning progresses, algorithmic processes will only become more incomprehensible to human beings, who will be relegated to merely relying on the outcomes of these processes, without having meaningful access to the data or its processing that these algorithmic systems rely upon to produce specific outcomes. But in the absence of government actors making clear and reasoned decisions, it will be impossible for courts to hold them accountable for their actions.

Stephen Hawking has said, "The development of full artificial intelligence could spell the end of the human race." Elon Musk claims that AI is humanity's "biggest existential threat." That might have people asking: Wait, what? But these grand worries are rooted in research. Along with Hawking and Musk, prominent figures at Oxford and UC Berkeley and many of the researchers working in AI today believe that advanced AI systems, if deployed carelessly, could end all life on earth. This concern has been raised since the dawn of computing. But it has come into particular focus in recent years, as advances in machine-learning techniques have given us a more concrete understanding of what we can do with AI, what AI can do for (and to) us, and how much we still don't know. Some of them think advanced AI is so distant that there's no point in thinking about it now. Others are worried that excessive hype about the power of their field might kill it prematurely. And even among the people who broadly agree that AI poses unique dangers, there are varying takes on what steps make the most sense today.

On a hot late-summer day in 2005, I sat in a packed, agreeably air-conditioned auditorium and listened to a university administrator welcome the class of 2009. As the popular saying goes, 'The rich go to Peking U, the poor go to Tsinghua, and the ones willing to work themselves to death come to USTC.'" If Peking University is China's Harvard, and Tsinghua is China's MIT, the University of Science and Technology of China, or USTC, is known as "the Caltech of China" for its small size and intense focus on science and engineering. I was proud to be there. But my pride shifted to awkwardness after the speech, when we stood to sing the university anthem, which ends with an exhortation: "Always learn from the people, and learn from the great leader Mao Zedong!" Hearing Mao's name left a bitter taste. It reminded me of career paths my country had denied me. Without the rule of law, I could not become a lawyer. Without a free press, I could not become a journalist. Without democratic elections, I could not become a politician. Instead I did what was expected of Chinese students without political connections or financial resources but with impeccable grades: I came to USTC to study science. The lyrics of the anthem brought up a question my classmates and I would often ponder: Must scientific research be in service of one's country--or can the pursuit of knowledge transcend nationalism? Generations of scientists at USTC have sought to answer this question. The university gave birth to both China's first satellite, launched in 1970, and the world's first quantum-communication satellite, launched in 2016. It is home to China's first synchrotron particle accelerator, and it will soon host a new multibillion-dollar quantum-science center. Over the years, faculty and students have, at times, wielded the university's scientific prestige as a shield to protect academic freedom and political independence. But if the university's rising trajectory in recent years is any indication, science in China thrives most when it serves the state. Today I live and work in the United States. I spoke to many old schoolmates and current USTC researchers to report this article. The story of USTC that emerges reveals the limits of science's ability to transcend China's authoritarian politics. It is also the story of my family across three generations. USTC was founded in Beijing in 1958, to train scientists for China's fledgling nuclear and space programs. Members of the faculty were drawn from China's scientific elite. Fang Lizhi, one of the first, came to teach physics after being deemed too politically outspoken to work on the bomb. "He was actually happy about it!

Even for a company as serially scandalous as Facebook, it's been a bad week for the social network. Separate investigations revealed that Facebook gave more than 150 firms access to people's private messages, while also making it impossible for users to avoid location-based ads. After months of fallout from the Cambridge Analytica scandal, US prosecutors also finally got around to filing a lawsuit against Facebook for its data sharing practices. Individually, none of these would likely be enough to bring Facebook down, but some experts believe that, collectively, this could signal the end for the internet behemoth. David Carroll, an associate professor at Parsons School of Design in New York, said this week may finally have dealt Facebook its "knockout" blow.

Why is AI important for Europe? As electricity did in the past, AI is transforming our world. AI is at our fingertips, when we translate texts online or use a mobile app to find the best way to go to our next destination. At home, a smart thermostat can reduce energy bills by up to 25% by analysing the habits of the people who live in the house and adjusting the temperature accordingly. In healthcare, algorithms can help dermatologists make better diagnoses: by detecting, for example, 95% of skin cancers by learning from large sets of medical images.