Self-driving cars, medical research, financial market predictions: The applications of machine learning technologies seem limitless. Data processing isn't a small task: Countless human work hours are spent collating and developing data, seeking patterns and information from seemingly unrelated tidbits. And there is a constantly growing number of data sources: Excel spreadsheets or SQL (or NoSQL) databases have long-since replaced the old-school filing cabinets of records, not to mention the growth in tracked website usage, which can improve web experience for users, as well as advertising options for companies. With all of these potential areas of growth, which industries can you expect to be most affected by advances in machine learning? Having personal exposure to emerging health-care tech, I can assure you we're in for some exciting times.

Hospitals have an understandable goal for their intensive care units: to reduce "dead in bed" events. With streams of data coming from equipment that monitors patients' vital signs, the ICU seems the perfect setting to deploy artificially intelligent tools that could judge when a patient is likely to take a turn for the worse. "A lot of hospitals are interested in developing early warning systems that can predict life-threatening events like sepsis, cardiac arrest, and respiratory arrest," says Priyanka Shah of the ECRI Institute, a nonprofit that evaluates medical procedures, devices, and drugs for the health care industry. Both academic researchers and medical device companies are now trying to figure out which combinations of measurements can provide the best indication of patient deterioration, Shah says. Once that technical challenge is met, researchers will still have to prove "clinical relevance," she says--not just proof that the technology works, but also that it can be integrated into a hospital's workflow and that it will save money.

Yogi Berra once said, "It's tough to make predictions, especially about the future." It is tough indeed, but enterprises that can make probabilistic predictions about customers, business processes, and operations will have an edge over enterprises that can't. These predictions don't have to be macroscopic to be consequential. Predictions about what a customer is likely to buy next. Predictions about marketing content that will resonate with a prospect.

By 2025, artificial intelligence (AI) technology will reduce the number of employees in asset management globally by 90,000, according to research. A new report authored by research and consulting firm, Opimas, found employees in capital markets globally will decrease by 230,000 and the asset management industry will likely shrink the most as a result of AI implementation. "AI will intensify clients' disenchantment with traditional asset managers and lead them increasingly to cheaper and automated strategies," it said. The report explained AI technology, including robotic process automation, machine learning and cognitive analytics, will significantly reduce headcounts across financial services firms. "Securities services firms, given the repetitive nature of many tasks conducted by them, are ideal candidates for a contraction of staff due to the rollout of AI technologies," it added.

Artificial intelligence is everywhere and it's here to stay. Most aspects of our lives are now touched by artificial intelligence in one way or another, from deciding what books or flights to buy online to whether our job applications are successful, whether we receive a bank loan, and even what treatment we receive for cancer. We may have things better than ever – but we've also never faced such world-changing challenges. That's why Future Now asked 50 experts – scientists, technologists, business leaders and entrepreneurs – to name what they saw as the key challenges in their area. The range of different responses demonstrate the richness and complexity of the modern world. Inspired by these responses, over the next month we will be publishing a series of feature articles and videos that take an in-depth look at the biggest challenges we face today.

This year, 2017, has been dubbed the year artificial intelligence (AI). With developers already beginning to innovate and improve the technologies that already exist, the potential growth in the field is undeniable. The question of strong artificial intelligence is not one of "if" but one of "when" and it is only a matter of time before artificial intelligence is fully integrated into our lives. There are two main types of artificial intelligence to keep an eye out for: strong and weak. With no signs of slowing down, AI integration is very much a real part of the world today.

If you don't have a Pixel, there's now one less reason to look at it with envy. The Google Assistant is coming to the smartphone that's in your hand. Google has released it from the safe confines of the Pixel and is rolling it out to any device that runs Marshmallow (6.0) or Nougat (7.0). Yes, you could always get the Google Assistant in Allo, but it was very limited in what was possible, and nobody wants to run a specific chat app for it. Now it's always ready to answer questions, find you photos, and connect more deeply to your Google data. If it's come to your phone, prepare for a major enhancement in how you use Android.

Stephen Hawking has warned that technology needs to be controlled in order to prevent it from destroying the human race. The world-renowned physicist, who has spoken out about the dangers of artificial intelligence in the past, believes we need to establish a way of identifying threats quickly, before they have a chance to escalate. "Since civilisation began, aggression has been useful inasmuch as it has definite survival advantages," he told The Times. "It is hard-wired into our genes by Darwinian evolution. Now, however, technology has advanced at such a pace that this aggression may destroy us all by nuclear or biological war. We need to control this inherited instinct by our logic and reason."

Led by Fei-Fei Li, the director of the Stanford University artificial intelligence lab and a newly minted Google employee, a team of academics recently explored a new way of tracking socioeconomic trends across the US. Rather than knocking on doors and asking questions, they pulled more than 50 million photos from Google Street View and fed them into neural networks. Simply by identifying the make, model, and year of automobiles appearing in the photos, the researchers said, their tech could accurately estimate the income, race, education, and voting patterns of citizens in particular precincts. If the number of sedans on a short stretch of road exceeded the number pickup trucks, for instance, they found that a city was 88 percent likely to vote for a Democrat during the next presidential election. If pickups exceeded sedans, a city was 82 percent likely vote Republican.

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