Artificial intelligence--the mere words bring on a myriad of conflicting emotions. Images of The Matrix swim before your eyes. That is until you remember how helpful Siri is when you can't put your finger on the name of a song on the radio. "You seem to be listening to'Come On Eileen' by Dexy's Midnight Runners, but don't ask me to sing it." Aside from the fact that you should probably know this song, Siri is both useful and playful, no doubt.

The term'Artificial Intelligence' was originally coined in the 1950s by the computer scientist John McCarthy. Human-style intelligence, is the desire for people to create human-like consciousness in a machine, enabling it to apply common sense, work out varied problems and even have emotional intelligence, sometimes referred to as'general' or'strong' AI, and Task-orientated intelligence, is the ability to do a limited range of tasks very well, such as the ability to drive a car, answer questions or to make health diagnoses, referred to as'narrow' or'weak' AI. Human-style intelligence, is the desire for people to create human-like consciousness in a machine, enabling it to apply common sense, work out varied problems and even have emotional intelligence, sometimes referred to as'general' or'strong' AI, and Task-orientated intelligence, is the ability to do a limited range of tasks very well, such as the ability to drive a car, answer questions or to make health diagnoses, referred to as'narrow' or'weak' AI. Today, the hype around artificial intelligence (AI) is ramping up, especially as big tech companies like Apple, Amazon, Google, Facebook, IBM and Microsoft attempt to commercialize its use. Digital Ad Agencies are also starting to figure out how they can leverage Artificial Intelligence techniques to make their clients' marketing and advertising efforts more effective. So far in 2016, Artificial Intelligence technology has grabbed headlines as the focus of Apple's first acquisition--in the form of Emotient Inc--and Facebook CEO Mark Zuckerberg has resolved to build an AI assistant to run his home and help him at work. Google has also gone down in the history books after its DeepMind team developed an AI program capable of defeating human world champions of complex Chinese board game Go. It's an achievement reminiscent of IBM's milestone moment when its cognitive system IBM's Watson thrashed human contestants in the U.S. game show Jeopardy in 2011. Watson was custom-built to process natural language and reason its way through information.

Working in the field of big data and AI means that I see the leading edge advances that come with it. It also means routinely getting freaked out when you think too closely about the possibilities and implications of those advances and where they might be taking us. Robots and AIs Will Take Our Jobs This isn't just science fiction, it's happening now. Manufacturing are the first places we see robots and automation eliminating human jobs, but it's hard to think of an industry that will be left unaffected as robots and AI become more affordable and widespread. It's estimated that between 35 and 50 percent of jobs that exist today are at risk of being lost to automation.

One of my favorite small projects, EmailFiler, was motivated by a school assignment for Georgia Tech's Intro to Machine Learning class. Basically, the assignment was to pick some datasets, throw a bunch of supervised learning algorithms at them, and analyze the results. But here's the thing: we could make our own datasets if we so chose. And so choose I did - to export my gmail data and explore the feasibility of machine-learned email categorization. See, I learned long ago that it's often best to keep emails around in case there is randomly some need to refer back to them in the future.

My last interview for this year is with Steve Lohr. Steve Lohr has covered technology, business, and economics for the New York Times for more than twenty years. In 2013 he was part of the team awarded the Pulitzer Prize for Explanatory Reporting. We discussed Big Data and how it influences the new Artificial Intelligence awakening. Steve Lohr: Both Google and Microsoft are contributing their tools to expand and enlarge the AI community, which is good for the world and good for their businesses.

In 2025, in accordance with Moore's Law, we'll see an acceleration in the rate of change as we move closer to a world of true abundance. Here are eight areas where we'll see extraordinary transformation in the next decade: In 2025, $1,000 should buy you a computer able to calculate at 10 16 cycles per second (10,000 trillion cycles per second), the equivalent processing speed of the human brain. The Internet of Everything describes the networked connections between devices, people, processes and data. By 2025, the IoE will exceed 100 billion connected devices, each with a dozen or more sensors collecting data. This will lead to a trillion-sensor economy driving a data revolution beyond our imagination.

Global Big Data Conference's vendor agnostic Global Artificial Intelligence(AI) Conference is held on January 19th, January 20th, & January 21st 2017 on all industry verticals(Finance, Retail/E-Commerce/M-Commerce, Healthcare/Pharma/BioTech, Energy, Education, Insurance, Manufacturing, Telco, Auto, Hi-Tech, Media, Agriculture, Chemical, Government, Transportation etc..). It will be the largest vendor agnostic conference in AI space. The Conference allows practitioners to discuss AI through effective use of various techniques. Large amount of data created by various mobile platforms, social media interactions, e-commerce transactions, and IoT provide an opportunity for businesses to effectively tailor their services by effective use of AI. Proper use of Artificial Intelligence can be a major competitive advantage for any business considering vast amount of data being generated.

The key to this new tool is that N2D2 doesn't just compare different hardware on the basis of recognition accuracy. It can compare hardware in terms of "processing time, hardware cost, and energy consumption." This is critical, said Duranton, because different applications for deep learning will likely require different parameters in various hardware implementations. The N2D2 offers benchmarking on a variety of commercial off-the-shelf hardware -- including multi/many-core CPUs, GPUs and FPGA. Barriers to edge computing As a research organization, CEA has been studying how best to bring deep neural networks to edge computing.

Harriet Green tells us she is very excited about the prospect of the cognitive era. And so she should be. The former Thomas Cook CEO, switched holidays for robots, when she joined IBM in 2015 to head up its Watson, Internet of Things, commerce and education department. "IoT is just an amazing force of the digitisation movement – connecting things to people," she tells Essential Retail. "It's really all about Watson's ability to take vast amounts of structured and unstructured data and process that data, whether its smell, sound, video or text."

IMAGE: This is the Architecture of the Adversarial Autoencoder (AAE). The study was published in Oncotarget on 22nd of December, 2016. The study represents the proof of concept for applying Generative Adversarial Networks (GANs) to drug discovery. The authors significantly extended this model to generate new leads according to multiple requested characteristics and plan to launch a comprehensive GAN-based drug discovery engine producing promising therapeutic treatments to significantly accelerate pharmaceutical R&D and improve the success rates in clinical trials. Since 2010 deep learning systems demonstrated unprecedented results in image, voice and text recognition, in many cases surpassing human accuracy and enabling autonomous driving, automated creation of pleasant art and even composition of pleasant music.