It is hard to miss the warnings. In the race to make computers more intelligent than us, humanity will summon a demon, bring forth the end of days, and code itself into oblivion. Instead of silicon assistants we'll build silicon assassins. The doomsday story of an evil AI has been told a thousand times. But behind the apocalyptic words of Stephen Hawking, the Oxford philosopher Nick Bostrom, and Elon Musk – who compared AI to nukes even as he launched an AI company – lies a more numbing existential threat.

Google's finally interweaving the deep treasure troves of information it holds about the world at large and you, specifically. Google Assistant is a conversational digital assistant built around the company's strengths in deep learning and natural language recognition, summoned at an utterance of "Ok Google" to respond to any queries you may have. It can understand context depending on the topic at hand, too: Holding your camera at a famous sculpture and asking "Who designed this?" will get an answer, as will queries like "Show me his other movies" after asking something like "Who directed The Revenant?" Google Assistant will be able to play music playlists, file reminders, help you buy movie tickets, and a whole lot more. Interestingly, it doesn't appear to be a discrete app; instead, it appears tied to be baked right into Google-y gadgets, in what Google CEO Sundar Pichai calls "an ambient experience that extends across devices." Think of it as a supercharged version of Google Now, mixed with Amazon's Alexa.

Big Data has been a hot topic since the Information Age took the world by storm in the 21st century. What quickly followed was the birth of the "data scientist" as an occupation to manage this influx of information, a job title our past VP of Product, DJ Patil coined in 2008. Having witnessed the growth of data science as a discipline, our Chief Data Scientist, Vitaly Gordon is excited for its future. Vitaly currently leads our data science team at SalesforceIQ, where they are developing sophisticated machine learning tools for the intelligent Customer Success Platform. We sat down with Vitaly to get his perspective on what he envisions for the future of data science.

Someday soon we'll sit Amazon's Echo and Google Home side by side on the kitchen counter and do the Pepsi Challenge to see which smart speaker is smarter and most helpful. We'll ask each of their voice-powered assistant services a series of questions. Give them a series of tasks to do. They'll read recipes, give traffic reports, talk about the weather, answer trivia questions. Or at least that's my best guess after seeing Home's unveiling at Google's I/O conference keynote on Tuesday.

In my article Voice and Mobile Search-the way forward, I have discussed that search is constantly evolving, as technology improves the lives of people and consumer behavior shifts. Search is getting more into local mode. Local consumers are now using mobile technology more that has affected how marketers engage with and convert the local shoppers who are always curious about deals and discounts. This means search must be an integral part of a strong marketing strategy in a smarter way which can't be a one-size-fits all approach and need to be planned meticulously to understand the consumer activities. Data available from social media channels is a great example of the way in which new digital technologies provide business with a more comprehensive understanding of the consumer.

Pepper, the robot that has been trained to "perceive" human emotion, is opening up its platform to Android developers. Maker SoftBank is hoping that it will spur new apps and new capabilities for the humanoid robot which has sold well but still has no clear defined purpose. Ten thousand of the robots have been sold but developers have been slow to make apps for its closed Naoqi operating system. Android will run on a tablet strapped to the robot's chest. Neither Google nor SoftBank has disclosed what sort of business deal they have struck and it is unclear if the robot will take advantage of new features such as the recently announced artificial intelligence Google Assistant. But it will almost certainly offer Google some degree of control over the robot as well as a cut of revenues.

In order to outperform more traditional machine learning methods, deep neural nets require large amounts of data and expertise with highly-parallel and high-performance graphics processing unit (GPU) computing. Insilico Medicine is working on over a dozen different applications of deep learning methods to regenerative medicine, embryonic development, cross-species comparison and drug discovery and repurposing providing contract research services and developing a range of molecules for cancer, metabolic and CNS pathologies. We want to minimize animal testing and simulate many biological processes in silico", said Putin, deep learning lead at Insilico Medicine, Inc. To develop a data set of blood biochemistry and cell count samples Insilico Medicine collaborated with the largest independent laboratory test service provider in Eastern Europe, Invitro Laboratories. Using this data set Insilco Medicine scientists then trained 40 different deep neural networks (DNNs) of different depth with a single neuron output predicting chronological age and optimized using different optimizers and started organizing these DNNs into an ensemble.

Neuroscientists at DeepMind, a Google-owned AI lab in London, have teamed up with academics at Oxford University and UCL to try and determine how the human brain navigates an underground train network. The group -- whose work was published in the journal Neuron this week -- asked humans to plan a journey in a virtual subway network. Participants were tasked with getting from A to B while MRI scans of their brain were taken. These scans showed which parts of the brain are involved in planning and making decisions. The group, which included Google DeepMind CEO Demis Hassabis, concluded that the brain splits the task of completing a journey into different jobs, with different parts of the brain handling different elements of the task.

Ratan Tata, chairman emeritus of Tata Sons pumped an undisclosed amount in Bangalore-based artificial intelligence startup niki.ai. Along with Mr. Tata, Existing investor, Ronnie Screwvala's Unilazer Ventures also participated in this funding round. Unilazer had invested in the chat App back on October 2015 and had made a commitment of a follow-on investment round of up to Rs 5 crore. "Chatbots have now picked up globally and Niki has been at the forefront of this innovation, building the technology for over a year now. We continue to support the team as we believe in their vision to simplify transactions for consumers," said Screwvala.

"It is exciting to see the power of deep learning applied to potential aging biomarkers. The availability of such markers is an essential prerequisite for any future clinical trials to try to ameliorate the effects of human aging", said Charles Cantor, PhD, CSO of Agena, Inc, former director of the Human Genome Project (DOE). The availability of big data coupled with advances in highly-parallel high-performance computing led to a renaissance in artificial neural networks resulting in trained algorithms surpassing human performance in image and voice recognition, autonomous driving and many other tasks. However, the adoption of deep learning in biomedicine and especially in the pharmaceutical industry has been reasonably slow. In order to outperform more traditional machine learning methods, deep neural nets require large amounts of data and expertise with highly-parallel and high-performance graphics processing unit (GPU) computing.