28 août 2019

Towards artificial general intelligence with hybrid Tianjic chip architecture

Towards artificial general intelligence with hybrid Tianjic chip architecture There are two general approaches to developing artificial general intelligence (AGI)1: computer-science-oriented and neuroscience-oriented. Because of the fundamental differences in their formulations and coding schemes, these two approaches rely on distinct and incompatible platforms2,3,4,5,6,7,8, retarding the development of AGI. A general platform that could support the prevailing computer-science-based artificial neural networks as well as... [Lire la suite]

01 mai 2019

This UK startup thinks it can win the self-driving car race with better machine learning

This UK startup thinks it can win the self-driving car race with better machine learning A new U.K. self-driving car startup founded by Amar Shah and Alex Kendall, two machine learning PhDs from University of Cambridge, is de-cloaking today. Wayve — backed by New York-based Compound, Europe’s Fly Ventures, and Brent Hoberman’s Firstminute Capital — is building what it describes as “end-to-end machine learning algorithms” to make autonomous vehicles a reality, an approach it claims is different to much of the... [Lire la suite]
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06 février 2019

Uber to set up its own artificial intelligence lab

Uber to set up its own artificial intelligence lab Uber has aquired a machine learning company and set up a new division dedicated to research in AI.  Uber AI Labs will be based in San Francisco and its initial core will be formed ofthe team of 15 from recently acquired Geometric Intelligence, an AI research startup.  Machine learning will be central to Uber's mission of using tech to "negotiate the real world", the company said.  "It manifests in myriad ways, from determining an optimal route... [Lire la suite]
02 février 2019

DEEP LEARNING FOR AUTOMOTIVE - A HETEROGENEOUS APPROACH

DEEP LEARNING FOR AUTOMOTIVE - A HETEROGENEOUS APPROACH The Artificial Intelligence is revolutionizing our world. Exponential growth in processing power in silicon and simultaneous reduction in its cost has created a new class of embedded technologies that have built-in Convolutional Neural Networks (CNNs). The CNN architecture is inspired by the neurons of the human brain. This is also known as ‘Deep Learning’, essentially because, layers of ‘Deep Neural Networks’ are trained using data sets from the real world. The ultimate goal... [Lire la suite]
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08 janvier 2019

The Evolution of Deep Learning for ADAS Applications

The Evolution of Deep Learning for ADAS Applications Embedded vision solutions will be a key enabler for making automobiles fully autonomous. Giving an automobile a set of eyes – in the form of multiple cameras and image sensors – is a first step, but it also will be critical for the automobile to interpret content from those images and react accordingly.  To accomplish this, embedded vision processors must be hardware optimized for performance while achieving low power and small area, have tools to program the hardware... [Lire la suite]
23 août 2018

Explainable Artificial Intelligence (XAI) : a new trend for Autonomous Driving

Explainable Artificial Intelligence (XAI) : a new trend for Autonomous Driving Autonomous Driving is a field of application of Artificial Intelligence, and especially of Deep Learning. But the fact is that Deep Learning cannot explain its behaviour. For Autonomous Driving, in case of accident, it is necessary to understand if the Autonomous Vehicle made a mistake and if yes, to know what this mistake is, why, and how to fix it. Then it is necessary to develop a new kind of Artificial Intelligence system called : eXplanable... [Lire la suite]

28 mai 2018

DEEP LEARNING FOR ONBOARD APPLICATIONS: HIDDEN TRAP

DEEP LEARNING FOR ONBOARD APPLICATIONS: HIDDEN TRAP Now Deep Learning is used in onboard detection and pattern recognition applications. NEXYAD for instance uses Deep Learning in RoadNex (road detection without need of markings + detection of free space), and ObstaNex (obstacles detection). But if you do not analyse your INDUSTRIAL project in detail, you may have bad surprises : everone thinks he/she knows that the more numerous the training examples, the most accurate the KPIs. Let's say you used 1 billion km to train and... [Lire la suite]
11 août 2017

Artificial Intelligence for Automotive Applications

Artificial Intelligence for Automotive Applications Software, Hardware, and Services for Autonomous Driving, Personalized Services, Predictive Maintenance, Localization and Mapping, Sensor Data Fusion, and Other Use Cases: Market Analysis and Forecasts REPORT DETAILS PRICE: Log In to View PAGES: 63 TABLES, CHARTS,     & FIGURES: 69 PUBLICATION DATE: 2Q 2017 Log In to Purchase Report DOWNLOADS Register or Log In to download a free Executive... [Lire la suite]
20 mai 2017

4 pillars of Artificial Intelligence that are building the car of the future

4 pillars of Artificial Intelligence that are building the car of the future Artificial Intelligence, autonomous vehicles, Deep Learning – these aren’t just buzzwords. The automotive industry is rapidly changing and in large part, it’s due to user demand. In these smarter, safer cars, it’s your interactions that will implicitly train the machine to learn your likes and dislikes – something made possible through these four pillars of AI. The automotive industry is rapidly changing. Autonomous and semi-autonomous driving are key... [Lire la suite]
31 mars 2017

NEXYAD presenting a methodology for machine learning / deep learning at French railways company SNCF conference

NEXYAD presenting a methodology for machine learning / deep learning at French railways company SNCF conference   The CEO of NEXYAD presented a methodology for machine learning and deep learning application in real industrial projects. This conference was organized by the French Railways company SNCF. Another startup of the MOV'EO Groupement ADAS presented a neural networks-based project : Global Sensing Technology.