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.

Tragically, something went wrong in 2015, but Holbrook never saw it coming. The tragedy devastated her husband, three children, grandchildren and dozens of coworkers who filled the back four pews at her funeral.

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Venture capital investments in artificial intelligence (AI), one of the biggest technological trends these days, are booming. The year 2016 saw almost ten times more funds invested in the space than 2012 did. Virtual and augmented reality (AR/VR), sometimes associated with the creative side of AI, also became a hot topic: More than $1.8 billion was invested in AR/VR, compared with just $86 million in 2012. Applying AI to solve tasks normally considered creative has historical precedent. For example, procedural generation has been used to draw textures, produce 3D models, and automatically generate large amounts of content in video games since the 1980s.

"I, Robot", a movie from 2004 set in the year 2035 where humans and robots coexist, but the humanoid robots are really just assistants and labor workers for humans. These robots run on an AI supercomputer called V.I.K.I, but all it took is for V.I.K.I to become self aware and turn the robots on the humans, much like "Terminator" with SkyNet, but with a bit less violence. The year is 2017 and we are not yet at the point where we have AI mastered enough to create robots like those in "I, Robot" and "Terminator", but we are getting very close. The point is, we'll more than likely see robots like those in "I, Robot" before the year of the movie, 2035. The way the technology is going in AI and robotics, we could see mass produced AI equipped robot assistants as early as 2025.

Turns out, machines can learn. And while that may mean the fall of man, it also means self-driving cars and better Netflix recommendations. Machine learning is a type of artificial intelligence (AI) that gives computers the ability to learn without being explicitly programmed. The implications of that are pretty massive: it means that a developer doesn't need to know, for example, how to program a computer to drive a car. But that developer can use a series of complex algorithms to program the computer to teach itself how to drive a car.

Fans of the innovative HBO series "Westworld" -- a futuristic tale of life-like robots mixing with guests of a Wild West-styled adult theme park -- can hear Wednesday about how close such technology is from a Rensselaer Polytechnic Institute professor involved in artificial intelligence research for the U.S. military. "'Westworld' is an HBO series that deals with the'big questions' of artificial intelligence (AI) in an undeniably vivid and timely way," said Selmer Bringsjord, director of the RPI Artificial Intelligence and Reasoning Lab. "The real world will ineluctably move toward giving experiences to humans in environments that are at once immersive and populated with sophisticated AIs and robots." Currently, Bringsjord is working on a multi-million dollar AI development project with support from the U.S. Office of Naval Research, which wants to advance military robotics for logistics and other missions. His work focuses on how to program a form of moral sense into AI, so that a robot not under continuous human control can make appropriate choices -- such as not harming innocent humans or causing unnecessary damage -- when faced with unexpected circumstances.

What is machine learning and how does it work? You've probably heard the buzz. The age of artificial intelligence has arrived. But that doesn't mean it's easy to wrap your mind around. Let's break down the basics of artificial intelligence, bots, and machine learning. Besides, there's nothing that will impact marketing more in the next five to ten years than artificial intelligence.

Kalashnikov Concern, Russia's largest weapons manufacturer, has plans to develop a massive 20-ton attack and spy robot, intends to break into the increasingly popular drone market and even develop video games, the company's top executive told TASS News Agency in a far-ranging interview published Tuesday. CEO Aleksey Krivoruchko touched on his company's plans to hit almost every part of the arms and technology sectors but also said an entire family of robots were in development and the progress would be unveiled later this year. "We are pushing ahead with this work and hope to show some of our achievements at the Army-2017," Krivoruchko said, referring to a military technology convention when asked about potential new robot projects. "I'm talking not about one vehicle but a whole family of reconnaissance and attack vehicles of different class that can operate as a team." After creating the Soratnik, an unmanned combat ground vehicle revealed in September, Krivoruchko was also asked if something even bigger was in the mix.

Pentaho helps data scientists and engineers easily prepare and blend traditional sources like ERP, EAM and big data sources like sensors and social media. Pentaho also accelerates the notoriously difficult and costly task of feature engineering by automating data onboarding, data transformation and data validation in an easy-to-use drag and drop environment. Model training, tuning and testing -- Data scientists often apply trial and error to strike the right balance of complexity, performance and accuracy in their models. With integrations for languages like R and Python, and for machine learning packages like Spark MLlib and Weka, Pentaho allows data scientists to seamlessly train, tune, build and test models faster. Model deployment and operationalization -- a completely trained, tuned and tested machine learning model still needs to be deployed.