Here are five things in technology that happened this past week and how they affect your business. Google Glass is back – but now it's called Lens. The company is making the device more feature-friendly and adding functionality that can help your business. For example, if your customers use Google Lens on an iOS or ARCore-compatible Android phone, they'll soon be able to order at your restaurant. Or your salespeople can use it to translate foreign languages on-the-go. Lens will be accessible in the Google Assistant, Google Photos, and main Google search apps as well as directly in the camera app on Pixel phones.

Despite significant advancements in genetics and modern imaging technology, for the vast majority of breast cancer patients, the diagnosis catches them by surprise. For some, it comes too late. Later diagnosis means aggressive treatments, anxiety and uncertain outcomes. Therefore, identifying patients at risk before the disease develops has been a central pillar to breast cancer research and effective early detection programs. A team from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) and Massachusetts General Hospital (MGH) has created a new deep learning model that can predict from a mammogram if a patient is likely to develop breast cancer in the future.

Last fall, a bit of nerdy controversy arose around AI chip startup NovuMind when the company announced its first low-power chip for processing neural networks. The company claimed that its patented design could natively process 3-D tensor data far more efficiently than other designs that require pre-processing the 3D tensors into 2D matrices. NovuMind portrayed its advantages in low-resolution and high-resolution environments, while typical (Resnet 50) benchmarks target medium resolution algorithms common in datacenters (where NovuMind has a smaller advantage). Now, recent customer wins and trial deployments may provide the trump card in the debate. Let's take a look at the company, its first product, and some case studies that seem to bear out NovuMind's dramatic performance/watt claims.

Arvind Gupta, Partner and Head, Management Consulting, KPMG in India, explains why HR functions are still reluctant to use AI, the co-effect of workplace culture and digital transformation on each other and the CIO's role in all of this. Edited excerpts: What factors are causing reluctance in adopting AI in HR functions? One of the main challenges that see's reluctance is the fact that employees' data are not present in one single location. In most cases, the data is distributed over many different data sets; and often the absence of one set in the analytics could lead to a totally wrong estimation. Another challenge is that the world of HR is not one where black and white decisions work.

The OECD Principles on Artificial Intelligence promote artificial intelligence (AI) that is innovative and trustworthy and that respects human rights and democratic values. They were adopted on 22 May 2019 by OECD member countries when they approved the OECD Council Recommendation on Artificial Intelligence. The OECD AI Principles are the first such principles signed up to by governments. Beyond OECD members, other countries including Argentina, Brazil, Colombia, Costa Rica, Peru and Romania have already adhered to the AI Principles, with further adherents welcomed. The OECD AI Principles set standards for AI that are practical and flexible enough to stand the test of time in a rapidly evolving field.

Kolkata: Ayuvedic beauty and wellness products makers are increasingly using artificial intelligence (AI) to create highly personalised products. Actually, the beauty and wellness industry seems to have come a long way since the ages of ayurvedic inheritance to the present times of AI. AI has made it possible to create personalized beauty products. And the trend is fast catching up. AI can take all the details and differences into account, including personal allergies, medical condition, past history, past treatments and current treatments, country, climate, skin colour, ethnic background and so on.

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The world produces about 2.5 quintillion bytes of data every day. Storing and transferring all of this enormous--and constantly growing--number of images, videos, Tweets, and other forms of data is becoming a significant challenge, one that threatens to undermine the growth of the internet and thwart the introduction of new technologies, such as the Internet of Things. Now, a team of researchers reports that an algorithm that uses a machine learning technique based on the human brain could ease that data clog by reducing the size of multimedia files, such as videos and images, and restoring them without losing much quality or information. Machine learning is a type of artificial intelligence, or AI. In a study, the researchers developed an algorithm that features a recurrent neural network to compress and restore data, according to C. Lee Giles, David Reese Professor of Information Sciences and Technology, Penn State, and an Institute for CyberScience associate.

Hollywood legend Robert Downey Jr. is putting down his Iron Man suit and taking up the bigger challenge of cleaning up the Earth. Iron Man's on-screen nemesis Thanos had a radical way of reducing humanity's impact on the Earth, but Downey Jr. hopes to use a more humane solution. Appearing on-stage at Amazon's new re:MARS (Machine Learning, Automation, Robotics and Space) conference in Las Vegas, Downey Jr. launched an initiative called Footprint Coalition. "I don't pretend to understand the complexities we face as a species, just because I portrayed a genius in my professional life. My scholastic achievement peaked at a correctional finishing," Downey Jr. joked, in reference to being incarcerated on drug charges and never making it past high school.

Amazon on Wednesday announced Alexa Conversations, a deep learning-based approach for developers to create natural voice experiences on Alexa. The toolset, currently in preview, lets developers build natural skill dialogs with fewer lines of code and less training data, Amazon said. To use Conversations, developers provide API access to their skills' functionality, a sample of dialogs annotated with the prompts that Alexa will say to say to the customer, and the actions they expect the customer to take. Alexa Conversations' AI finishes the job, using the input data to generate dialog flows and variations. "It's way easier to build a complex voice experience with Alexa Conversations due to its underlying deep-learning-based dialog modeling,"said Rohit Prasad, Alexa vice president and head scientist, at Amazon's re:MARS conference in Las Vegas Wednesday.