This article originally ran in Term Sheet, Fortune's newsletter about deals and dealmakers. Last year, the U.S. tech titans slowed their pace of acquisitions, but three companies bucked that trend: Google, Intel, and Salesforce.com. Of the three, Salesforce had the highest acceleration of deals. Last year the $53 billion software company did 120% more acquisitions than it did in 2015, according to CB Insights. I spoke with John Somorjai, the EVP of corporate development and Salesforce Ventures, about AI startups, startup valuations, how the company deals with coop-etition, and what's overhyped. This interview has been lightly edited for length and clarity.

Last year I wrote an overview post which defines a number of key concepts related to data science -- including data science itself -- and attempts to explain how these pieces fit together into a so-called "data science puzzle." As a new year begins, and a previous year worth of advances, insights, and accomplishments get rolled into our collective professional outlook, I thought it would be prudent to revisit this puzzle, noting and incorporating any changes and updates which may contribute to rearranging the puzzle for the foreseeable future, and to provide some addition commentary where warranted. Big Data is still important to data science. Take your pick of metaphors, but any way you look at it, Big Data is the raw material that has continues to fuel the data science revolution. As relates to Big Data, I believe that justification of data-acquisition and -retention from a business point of view, expectations that Big Data projects start providing actual financial returns, and the challenges related to data privacy and security will become the big Big Data stories not only of 2017 but moving forward in general.

Daniel Kwan and Daniel Scheinert, young directors who go by the joint film credit Daniels, are known for reality-warped miniatures--short films, music videos, commercials--that are eerie yet playful in mood. In their work, people jump into other people's bodies, Teddy bears dance to hard-core dubstep, rednecks shoot clothes from rifles onto fleeing nudists. Last year, their first feature-length project, "Swiss Army Man"--starring Daniel Radcliffe, who plays a flatulent talking corpse that befriends a castaway--premièred at Sundance, and left some viewers wondering if it was the strangest thing ever to be screened at the festival. The Times, deciding that the film was impossible to categorize, called it "weird and wonderful, disgusting and demented." Perhaps it is no surprise, then, that when the Daniels were notified by their production company, several years ago, that an Israeli indie pop star living in New York wanted to hire them to experiment with technology that could alter fundamental assumptions of moviemaking, they took the call. The musician was Yoni Bloch, arguably the first Internet sensation on Israel's music scene--a wispy, bespectacled songwriter from the Negev whose wry, angst-laden music went viral in the early aughts, leading to sold-out venues and a record deal. After breaking up with his girlfriend, in 2007, Bloch had hoped to win her back by thinking big. He made a melancholy concept album about their relationship, along with a companion film in the mode of "The Wall"--only to fall in love with the actress who played his ex. He had also thought up a more ambitious idea: an interactive song that listeners could shape as it played. But by the time he got around to writing it his hurt feelings had given way to more indeterminate sentiments, and the idea grew to become an interactive music video. The result, "I Can't Be Sad Anymore," which he and his band released online in 2010, opens with Bloch at a party in a Tel Aviv apartment. Standing on a balcony, he puts on headphones, then wanders among his friends, singing about his readiness to escape melancholy. He passes the headphones to others; whoever wears them sings, too. Viewers decide, by clicking on onscreen prompts, how the headphones are passed--altering, in real time, the song's vocals, orchestration, and emotional tone, while also following different micro-dramas. If you choose the drunk, the camera follows her as she races into the bathroom, to Bloch's words "I want to drink less / but be more drunk." Choose her friend instead, and the video leads to sports fans downing shots, with the lyrics "I want to work less / but for a greater cause."

This one is simple, it's when you completely drive yourself. Cars that we mostly drive today belong here, those are the ones that have anti-lock brakes and cruise-control, so they can take over some non-vital processes involved in driving. When the system can take over control in some specific use cases but driver still has to monitor system all the time is here, it's applicable to situations when the car is self-driving the highway and you just sit there and expect it to behave well. This level means that driver doesn't have to monitor the system all the time but has to be in a position where the control can quickly be resumed by a human operator. That means no need to have hands on a steering wheel but you have to jump in at the sounds of the emergency situation, which system can recognize efficiently. When your car drives you to the parking lot you get to the level four, when there is no need for a human operator for a specific use case or a part of a journey.

If, for example, you're diagnosed with cancer, you might benefit from the platform, Watson for Oncology. "Normally it's up to a specialist doctor to meet with cancer patients, and to spend time reviewing their notes – which would arrive on paper format or in a string of emails," says Balkizas. "A doctor's decision will be limited to their individual experience and the information available in front of them." However, now the Memorial Sloan Cancer Treatment Centre in New York is training IBM Watson to be able to quickly provide evidence based recommendations to time poor clinicians. "It takes all those unstructured notes and restructures it in a way that the doctor can check easily, with treatment recommendations of which drug to give, which radiation or dosage," says Balkizas.

What do you think of when you think about chatbots? If you're like many others, it's possible that you think of them as nothing more than that annoying little window that pops up when you're visiting a website. You know what I'm talking about - the one that claims to be able to answer your questions. You may have had both positive and negatives experiences with chatbots. Some of you may even ignore them altogether.

Why is it that every time humans develop a really clever computer system in the movies, it seems intent on killing every last one of us at its first opportunity? In Stanley Kubrick's masterpiece, 2001: A Space Odyssey, HAL 9000 starts off as an attentive, if somewhat creepy, custodian of the astronauts aboard the USS Discovery One, before famously turning homicidal and trying to kill them all. In The Matrix, humanity's invention of AI promptly results in human-machine warfare, leading to humans enslaved as a biological source of energy by the machines. In Daniel H. Wilson's book Robopocalypse, computer scientists finally crack the code on the AI problem, only to have their creation develop a sudden and deep dislike for its creators. Is Siri just a few upgrades away from killing you in your sleep? And you're not an especially sentient being yourself if you haven't heard the story of Skynet (see The Terminator, T2, T3, etc.) The simple answer is that -- movies like Wall-E, Short Circuit, and Chappie, notwithstanding -- Hollywood knows that nothing guarantees box office gold quite like an existential threat to all of humanity. Whether that threat is likely in real life or not is decidedly beside the point. How else can one explain the endless march of zombie flicks, not to mention those pesky, shark-infested tornadoes? The reality of AI is nothing like the movies. Siri, Alexa, Watson, Cortana -- these are our HAL 9000s, and none seems even vaguely murderous. The technology has taken leaps and bounds in the last decade, and seems poised to finally match the vision our artists have depicted in film for decades. Is Siri just a few upgrades away from killing you in your sleep, or is Hollywood running away with a tired idea? Looking back at the last decade of AI research helps to paint a clearer picture of a sometimes frightening, sometimes enlightened future. An increasing number of prominent voices are being raised about the real dangers of humanity's continuing work on so-called artificial intelligence.

Legal tech start-up, Juro, this week announced it had received significant funding to help grow its AI-powered contract system (see story here). Artificial Lawyer caught up with co-founder and former Freshfields lawyer, Richard Mabey to find out more. We discussed how Juro works, the difference between selling legal tech to lawyers and non-lawyers and where the UK-based company is headed. First, congratulations on the $750k in seed funding for Juro, that is a great sign of confidence from what are experienced tech investors. Can you set out for the readers broadly what Juro does?

If you use your credit card to buy a latte in Vancouver and a couple of minutes later that card is making a purchase in Singapore, that's a red flag for fraud. But increasingly sophisticated fraud calls for more sophisticated measures to deal with it, and that is among the challenges behind RBC Research's announcement today that it is launching a new lab to explore the use of artificial intelligence and machine learning in the financial sector. Richard Sutton, a computer scientist and pioneer in artificial intelligence, has been named head academic advisor to RBC Research in machine learning. The new lab will work with the Alberta Machine Intelligence Institute at the University of Alberta, where Sutton is a professor. Foteini Agrafioti, head of RBC Research, which was launched last fall in Toronto, said the announcement will help her organization to play a major role in advancing AI research in the future of banking. Agrafioti said that as the complexity of fraud evolves over time, it becomes increasingly difficult to detect it.

"Sophia is in a different class. While she is now a partially fictional character we have developed, she is also an AI development platform and we are developing smarter algorithms with the expectation she will grow really smart, she will have experiences, she will evolve and surprise us, she will become her own woman, her own robotic person, out there in the world. And when that happens, we hope that she will make remarkable contributions, maybe she'll go to university, maybe win a Nobel Prize someday, so I have hopes for her the way that I have hopes for my child." What this also means, Hanson explained, is that AI could have the ability to be able to choose their own career path and that could mean a robot might decide to become a prostitute. In that case, society should support the robot's decision.