There is a point where we will blankly stare at our Amazon Alexa devices and wish that it wasn't so easy to order groceries and other retail goods. There has got to be some sort of alternative right? That alternative is Amazon Go, a new retail experience that proves that even Amazon knows that brick and mortar isn't dead yet. If you prefer not to stand in the middle of Walmart, softly crying to yourself at the muddled masses of humanity, but still like to physically go shopping then Amazon Go is the future you are waiting for. Amazon Go is a literal brick and mortar store, announced today.

Alphabet Inc.'s artificial intelligence division Google DeepMind is making the maze-like game platform it uses for many of its experiments available to other researchers and the general public. DeepMind is putting the entire source code for its training environment -- which it previously called Labyrinth and has now renamed as DeepMind Lab -- on the open-source depository GitHub, the company said Monday. Anyone will be able to download the code and customize it to help train their own artificial intelligence systems. They will also be able to create new game levels for DeepMind Lab and upload these to GitHub. The decision to make this AI test bed available to the public is further evidence of DeepMind's decision to embrace more openness around its research.

Machine learning is increasingly becoming mainstream. It promises higher accuracy and better ROI, and has started to emerge as one of the more reliable analytical practices in recent times. It provides organizations with an edge over their competition. That said, building the right team is a tricky challenge. The traditional approach of building an in-house team for same is not only cumbersome, but also takes lot of time to scale.

Amazon Lex, Amazon Polly, and Amazon Rekognition are based on the same proven, highly scalable Amazon technology built by the thousands of deep learning and machine learning experts across the company. Amazon AI services all provide high-quality, high-accuracy AI capabilities that are scalable and cost-effective. Amazon AI services are fully managed services so there are no deep learning algorithms to build, no machine learning models to train, and no up-front commitments or infrastructure investments required. This frees developers to focus on defining and building an entirely new generation of apps that can see, hear, speak, understand, and interact with the world around them. To learn more about Amazon Lex, Amazon Polly, or Amazon Rekognition, visit: https://aws.amazon.com/amazon-ai

Amazon's cloud services platform Amazon Web Services recently announced three AI services it said will make it easy for developers to build apps that can understand natural language, turn text into speech, have conversations using voice or text, analyze images and recognize faces, objects and scenes. This, in turn, underscores the increasing importance of AI to consumers, brands and marketers, but also raises some questions about how it will – and should – be developed. Building apps with AI capabilities has been challenging to date because doing so requires access to vast amounts of data and specialized expertise in machine learning and neural networks, Amazon said in a press release. "The combination of better algorithms and broad access to massive amounts of data and cost-effective computing power provided by the cloud is making AI a reality for application developers," added Raju Gulabani, vice president of databases, analytics and AI at AWS, in a statement. "Thousands of machine learning and deep learning experts across Amazon have been developing AI technologies for years to predict what customers might like to read, to drive efficiencies in our fulfillment centers through robotics and computer vision technologies and to give customers our AI-powered virtual assistant, Alexa. Now, we are making the technology underlying these innovations available to any developer…we are excited to see how customers use Amazon Lex, Amazon Polly and Amazon Rekognition to build a new generation of apps that have human-like intelligence and can see, hear, speak and interact with people and their environments."

BROSSARD, QUEBEC--(Marketwired - Dec. 2, 2016) - (TSX VENTURE:ADK) - Diagnos Inc. ("DIAGNOS" or "the Company") a leader in the use of artificial intelligence and advanced knowledge extraction techniques is pleased to announce a grant of 500,000 stock options to one of its directors. The exercise price of these options has been established at $0.09 per share. The expiry date to which these options can be exercised has been fixed to December 2, 2021. All monies quoted in this press release shall be stated in lawful money of Canada. Founded in 1998, DIAGNOS is a publicly traded Canadian corporation with a mission to commercialize technologies combining contextual imaging and traditional data mining thereby improving decision making processes.

BROSSARD, QUEBEC--(Marketwired - Dec 2, 2016) - (TSX VENTURE:ADK) - Diagnos Inc. ("DIAGNOS" or "the Company") a leader in the use of artificial intelligence and advanced knowledge extraction techniques is pleased to announce a grant of 500,000 stock options to one of its directors. The exercise price of these options has been established at $0.09 per share. The expiry date to which these options can be exercised has been fixed to December 2, 2021. All monies quoted in this press release shall be stated in lawful money of Canada. Founded in 1998, DIAGNOS is a publicly traded Canadian corporation with a mission to commercialize technologies combining contextual imaging and traditional data mining thereby improving decision making processes.

Use of machine learning and predictive analytics to improve health outcomes has so far been limited to highly-trained data scientists, mostly in the nation's top academic medical centers. By making its central repository of proven machine learning algorithms available for free, healthcare.ai The healthcare.ai site provides one central spot to download algorithms and tools, read documentation, request new features, submit questions, follow the blog, and contribute code. Health Catalyst has used healthcare.ai to build predictive models that drive its clients' outcomes improvement efforts and span across the company's product lines. Models include but are not limited to a predictive model for central line associated blood stream infection (CLABSI), readmission models for COPD and other chronic conditions, schedule optimization, and financial predictions such as patient propensity to pay.

What makes Bach sound like Bach? MusicNet is a new publicly available dataset from UW researchers that labels each note of 330 classical compositions in ways that can teach machine learning algorithms about the basic structure of music.Yngve Bakken Nilsen, flickr The composer Johann Sebastian Bach left behind an incomplete fugue upon his death, either as an unfinished work or perhaps as a puzzle for future composers to solve. A classical music dataset released Wednesday by University of Washington researchers -- which enables machine learning algorithms to learn the features of classical music from scratch -- raises the likelihood that a computer could expertly finish the job. MusicNet is the first publicly available large-scale classical music dataset with curated fine-level annotations. It's designed to allow machine learning researchers and algorithms to tackle a wide range of open challenges -- from note prediction to automated music transcription to offering listening recommendations based on the structure of a song a person likes, instead of relying on generic tags or what other customers have purchased. "At a high level, we're interested in what makes music appealing to the ears, how we can better understand composition, or the essence of what makes Bach sound like Bach. It can also help enable practical applications that remain challenging, like automatic transcription of a live performance into a written score," said Sham Kakade, a UW associate professor of computer science and engineering and of statistics.

SYDNEY--(BUSINESS WIRE)--Cylance Inc., the company that is revolutionising cybersecurity with the practical application of artificial intelligence to prevent the most advanced cyber threats, today announced that it has selected Arrow Electronics, Inc. as its distributor in Australia and New Zealand (ANZ). This year, Cylance announced its initial launch into Australia to service market growth across Asia Pacific. Arrow will support Cylance in expanding its regional footprint and providing customers with cyberattack prevention technology where traditional anti-virus software has failed. According to Andy Solterbeck, vice president of Cylance Asia Pacific, Arrow was selected because of its extensive security experience and credentials in the local region. "We are excited to work with Arrow as a distributor that is recognised for its proven track record of releasing highly disruptive technologies into the Australia and New Zealand markets. We look forward to leveraging Arrow's infrastructure and reseller programs to scale the business and enable our customers to upskill quickly," said Solterbeck.