Any great sci-fi movie has artificial intelligence (AI), but to be entertaining, a movie needs drama. So in the real world, advances in AI are less about robot overlords and more about "Siri, take me home." Below, a few members of Forbes Technology Council offer their insights on why we should all stop worrying about AI, and possibly even embrace it as the future. Pop culture always portrays AI as an attempt to reproduce human consciousness - computers becoming self-aware and then turning on their human masters. In practice, there are very few groups in academia or industry that are trying to address machine consciousness.

It's a total understatement to say that the growing presence of machine learning in games is big news. The technology promises to turn the industry topsy-turvy by changing our perception of what's possible in a gaming experience. "You can actually tailor game design and tailor levels to an individual's experience," George Dolbier, the CTO of Interactive Media at IBM, said at a talk at the Intel Buzz Workshop in Seattle last June. More interesting for us laymen, it's being used to create progressional curves that tailor themselves directly to each player according to their individual behavioral data. A simple example would be in a Tetris-like casual game where a player would first go through a few "seed" rounds of gameplay so that the machine learning element can first learn about how they play before it creates a customized roadmap that will ensure maximum engagement.

Machine learning, and by extension deep learning, has without a doubt seen its influence across many different industries. Even if you have never worked with it, and know nothing about it, chances are that you have used it; either directly through a chat-bot or indirectly in detection of spam and malware. This piece will largely be based on a a talk by Andrew NG about this very topic, he talks about two trends in the deep learning community; scale and end to end deep learning. For most people even mildly interested in deep learning, scale wouldn't come as a surprise. Over the last 10 to 20 years we have acquired a lot more data.

Chatbots are everywhere, if you know where to look for them. On Facebook Messenger alone, there are over 11,000 bots available for user interaction. They aren't perfect examples of AI, yet, and the argument throughout the tech world revolves around a single question: What makes a bot intelligent? Machine Learning and Natural Language Processing both have a place in bot intelligence, but what it all comes down to is how users perceive the bot's intelligence. What we've learned from the first wave of successful personal assistants like Siri, Google now, Alexa and Cortana, is that users perceive the AI as intelligent when they can ask simple questions and retrieve relevant information, which allows the users to achieve their goals in response to the question.

Salesforce CEO Marc Benioff said at a recent conference: "This is a huge shift going forward, which is that everybody wants systems that are smarter, everybody wants systems that are more predictive, everybody wants everything scored, everybody wants to understand what's the next best offer, next best opportunity, how to make things a little bit more efficient." Machine Learning (ML) and AI powering "Systems that Learn at scale" are at the bleeding edge of data science, deep learning and predictive search today. Everyone is jumping on this AI enabled engagem ent ("ambient reality, experience and convenience") trend in retail, banking and even healthcare. Facebook is a case study of where AI/ML are being used to augment user engagement and experiences. I am starting to see many leading firms investing in ML Accelerators and Platforms as part of their data science strategy.

The moment of the next revolution in the history of mankind is fast approaching – the difference between this and all previous revolutions, both social and economic, is that this time we won't be in it alone. We will be accompanied by complex computer systems carrying out activities, which, due to our cognitive abilities, were previously reserved only for human beings. The era of artificial intelligence is here. Probably the most famous examples of the application of artificial intelligence, though rather simplistic, were the chess duels between grandmaster Garry Kasparov and IBM computer Deep Blue in 1996 and 1997. These duels were supposed to show that a machine can not only perform certain actions similar to a human being, but also outperform its human counterpart.

What is it like to compete in a car race that isn't about speed, but rather fuel efficiency? As more than 100 people found out on March 3 at the Sonoma Raceway: really fun. A broad cross-section of the Silicon Valley community participated in Toyota Onramp 2017, an annual event hosted by Toyota Research Institute (TRI). The cornerstone of the event was the Prius Challenge, where the teams competed to see how efficiently they could drive nine laps at Sonoma Raceway. The competition also featured a special guest, Toyota Chairman of the Board Takeshi Uchiyamada.

DB Networks pioneered Artificial Intelligence based database security. Its customers include the world's largest financial institutions, healthcare providers, manufacturers, and governments. Customers gain insights by discovering all active databases, identifying tables being accessed, and the specific applications accessing the databases. In addition, analyzing application database access that deviates from the model of normal application behavior immediately identifies compromised credentials and database attacks. DB Networks is a privately held company headquartered in San Diego, Calif. For more information, call (800) 598-0450 or visit the company's website.

We now know that artificial intelligence (AI) can play a winning game of poker. But, alas, poker is a game of deceit, where one needs to remain calm and hold back any traces of emotions. In today's workplaces, it also helps to remain calm and hold back on emotions -- to an extent. But how many successful business leaders go through their days with constant poker faces? Alas, AI can't bring any of that good stuff to the table as of yet, and that is making it a hard sell.

As artificial intelligence becomes more powerful, people need to make sure it's not used by authoritarian regimes to centralize power and target certain populations, Microsoft Research's Kate Crawford warned on Sunday. In her SXSW session, titled Dark Days: AI and the Rise of Fascism, Crawford, who studies the social impact of machine learning and large-scale data systems, explained ways that automated systems and their encoded biases can be misused, particularly when they fall into the wrong hands. "Just as we are seeing a step function increase in the spread of AI, something else is happening: the rise of ultra-nationalism, rightwing authoritarianism and fascism," she said. All of these movements have shared characteristics, including the desire to centralize power, track populations, demonize outsiders and claim authority and neutrality without being accountable. Machine intelligence can be a powerful part of the power playbook, she said.