Business use of AI grew 270% over the past four years, according to Gartner, while Deloitte says 62% of respondents to its corporate October 2018 report deployed some form of AI. That's up 53% from a year ago, but what we've learned is that adoption doesn't equal success, and success is an evolving model in this phase of our digital revolution. Unfortunately, roughly 25% of companies have seen half of their AI projects fail. Failure, in heavily technical deployments, like AI projects, is incredibly expensive when data scientist and other team time, technical cost of computation, and resources wasted is accounted for. Statistics like these have generated tremendous buzz around the end results: success or failure, but we've reached a pivot point where we must widen our lens and shift our attention.

Newswire) GBT Technologies Inc. (OTCPINK: GTCH) ("GBT", or the "Company"), a company specializing in the development of Internet of Things (IoT) and Artificial Intelligence (AI) enabled networking and tracking technologies, including its GopherInsight wireless mesh network technology platform for both mobile and fixed solutions, announced that it is now implementing a new approach within its intelligent agent, recurrent relational reasoning (RRN). The new set of algorithms enables GBT's AI system to explicitly consider relations between objects (Static, moving), or abstract ideas. The RRN methodology will be implemented within Avant! AI within the next months, enabling it with logic analysis boost to handle vast information and data interpretation complexity. One of the key reasons for implementing this new method is to achieve outstanding image-based reasoning tasks for Avant!

National Crime Records Bureau (NCRB) under the Government of India has released a tender asking for bidders to help create Automated Facial Recognition System (AFRS). The objective is to leverage the power of facial recognition technology to make the security forces more efficient. The technology system which has been proposed will make an extensive database of photos belonging to Indian citizens using which machine learning models will be trained. The criminals will be identified using CCTV footage from the database, verified, and information will be distributed to all law enforcement agencies in real-time across the country. Experts have touted that using facial recognition technology to identify and solve crime may be one of the best applications of the technology.

Facebook today released the latest version of its deep learning library PyTorch with quantization and Google Cloud TPU support for speedier training of machine learning models. Tensor processing unit support begins with the ability to train a model with a single chip and will later be extended to Cloud Pods, Facebook CTO Mike Schroepfer said today. Also new today are PyTorch Mobile for deployment of ML on edge devices starting with Android and iOS devices; CryptTen, a tool for encrypted machine learning; and Captum, a tool for explainability of machine learning models. The news is being announced at the PyTorch Developer Conference today at The Midway in San Francisco. Available today, PyTorch 1.3 comes with the ability to quantize a model for inference on to either server or mobile devices.

Facebook today released the latest version of its deep learning library PyTorch with quantization and Google Cloud TPU support for speedier training of machine learning models. Tensor processing unit support begins with the ability to train a model with a single chip and will later be extended to Cloud Pods, Facebook CTO Mike Schroepfer said today. Also new today are PyTorch Mobile for deployment of ML on edge devices starting with Android and iOS devices; CryptTen, a tool for encrypted machine learning; and Captum, a tool for explainability of machine learning models. The news is being announced at the PyTorch Developer Conference today at The Midway in San Francisco. Available today, PyTorch 1.3 comes with the ability to quantize a model for inference on to either server or mobile devices.

Department of Electrical & Computer Engineering (ECE) welcomed Professor Nicolas Papernot as its newest faculty member this fall. He joins ECE from Pennsylvania State University after spending a year at Google Brain as a research scientist. We sat down with Professor Papernot to hear about his research, why he chose ECE at U of T and asked him what advice he had for the class of 2T3. You joined us after spending a year at Google. Can you tell us a bit about your academic history?

IBM today announced the 2019 Call for Code grand prize was awarded to Prometeo for developing a health monitoring platform for firefighters. The Barcelona-based team consisting of a nurse, a firefighter, and three developers will receive $200,000 and assistance from IBM and its partners to bring the project to life. TNW's finance, blockchain, and business event is coming up soon Promoteo began as an endeavor by firefighter Joan Herrera. Realizing there were no systems in place to monitor the health of firefighters combating wildfires, Herrera and nurse Vicenç Padró began collecting data by hand. Eventually, they joined forces with three IT professionals, Salomé Valero, Josep Ràfols, and Marco Rodriguez, and the team joined the Call For Code challenge.

Artificial intelligence sparks images of thinking robots doing things without human intervention. While that is part of its promise, reality is much more complex. Join me as I explore the intricacies of ethics, technology, policy and politics with three experts on this somewhat arcane but strategically crucial subject. This interview offers something close to a master class in AI, exploring why the US is not likely to use autonomous robots to kill the enemy, why Congress must come to grips with the many challenges AI raises, how the National Geospatial Intelligence Agency and the Intelligence Community are grappling with the unique issues of classification, fast access to data and the need for global access across the force, how companies like Booz Allen Hamilton are managing the data they need to train AIs and much more. The one thing Boris Johnson and his DUDE -- Deliver, Unite, Defeat, Energize -- partisans have in common with their opponents is that they all want to see a quick end to a process that has paralyzed the debate about Europe's future and created a climate of instability which only NATO's foes can find suitable.

Jean Metz started his career in academia, first as a PhD student and later as Adjunct Professor at a Federal University, where he co-created and led the Computational Intelligence Research Group. Since day one, he focused his research on Machine Learning and its applications. After a few years of academic life, he decided to make a career move and landed at the software industry. Now, he can apply his knowledge and skills on more tangible problems. He has been working as a consultant in Belgium for a few years, where he has helped companies to implement intelligent applications.

The wind of change is blowing across Venture Capital. Not confined to a specific market or geography, the relationship between VCs and the companies they invest in has shifted culturally and economically. There is a sense that models are in need of a change to safeguard otherwise resilient sectors such as tech from the fated'bubble' scenario that many sceptics see on the horizon, and with this, the deal structures and ultimately ROI is also changing. With a series of disappointing, or worse still, failed tech IPOs hitting the market over the past year, a rather grey cloud is beginning to shadow the world of start-up financing. You needn't look far to see how even the biggest names in the market have got it wrong; Uber, Lyft, WeWork, Slack, and Spotify are all trading way off their initial listings – which for their venture backers isn't boding well for forecasted targeted IRR.