The context: One of the greatest unsolved flaws of deep learning is its vulnerability to so-called adversarial attacks. When added to the input of an AI system, these perturbations, seemingly random or undetectable to the human eye, can make things go completely awry. Stickers strategically placed on a stop sign, for example, can trick a self-driving car into seeing a speed limit sign for 45 miles per hour, while stickers on a road can confuse a Tesla into veering into the wrong lane. Safety critical: Most adversarial research focuses on image recognition systems, but deep-learning-based image reconstruction systems are vulnerable too. This is particularly troubling in health care, where the latter are often used to reconstruct medical images like CT or MRI scans from x-ray data.
The practice to include Artificial Intelligence in industry application is skyrocketing for a decade now. It is evident since, AI and its constituent applications Machine Learning, computer vision, facial analysis, autonomous vehicles, deep learning form the pillars of modern digital empowerment. The ability to learn the data it is trained up to understand the binary, quantum computation of the world, and make decisions derived from its insights makes AI unique than earlier technologies. Leaders believe that possessing AI-based technologies equate to future industry successes. From healthcare, research, finance, logistics to military, law enforcement department AI holds the key to massive competitive edge and up-gradation with monetary benefits too.
With the rise of autonomous vehicles, smart video surveillance, facial detection and various people counting applications, fast and accurate object detection systems are rising in demand. These systems involve not only recognizing and classifying every object in an image, but localizing each one by drawing the appropriate bounding box around it. This makes object detection a significantly harder task than its traditional computer vision predecessor, image classification.
Click here to learn more about Gilad David Maayan. There are a significant number of investments in the automotive industry nowadays. The majority of these investments focus on artificial intelligence (AI) and the optimization of self-driving technology. Meanwhile, new mobility systems and players are making their way into the automotive market. Tesla is trying to improve its autopilot system, Uber is testing robo-taxis, and Google is developing self-driving cars.
Amazon recently bought up a self-driving autonomous ride-hailing startup Zoox, which is being claimed as the most ambitious step that the tech giant has taken in the recent past. Reportedly a $1.2 billion deal, the acquisition of the Robo-taxi company is not just to build upon its capabilities to deliver packages but actively set foot in the autonomous driving industry. While Amazon has invested heavily in developing drones or autonomous delivery robots in the past, its investment in self-driving vehicles has recently gained traction. Some of the other ventures of the company have been in self-driving truck Embark when CNBC reported that it had been hauling Amazon cargo on some of its test runs. For instance, in drones, Amazon has designed a future delivery system to safely deliver packages to customers in a short period of time.
To better understand the landscape of available tools for machine learning production, I decided to look up every AI/ML tool I could find. After filtering out applications companies (e.g. companies that use ML to provide business analytics), tools that aren't being actively developed, and tools that nobody uses, I got 202 tools. Please let me know if there are tools you think I should include but aren't on the list yet! The landscape is under-developed IV. I categorize the tools based on which step of the workflow that it supports. I don't include Project setup since it requires project management tools, not ML tools.
Can AI function like a human brain? But now, armed with Neuromorphic Computing, they are ready to show the world that their dream can change the world for better. As we unearth the benefits, the success of our machine learning and AI quest seem to depend to a great extent on the success of Neuromorphic Computing. The technologies of the future like autonomous vehicles and robots will need access to and utilization of an enormous amount of data and information in real-time. Today, to a limited extent, this is done by machine learning and AI that depend on supercomputer power.
We were delighted to be joined by Lex Fridman at the San Francisco edition of the Deep Learning Summit, taking part in both a'Deep Dive' session, allowing for a great amount of attendee interaction and collaboration, alongside a fireside chat with OpenAI Co-Founder & Chief Scientist, Ilya Sutskever. The MIT Researcher shared his thoughts on recent developments in AI and its current standing, highlighting its growth in recent years. Lex then referenced, Lee Sedol, the South Korean 9th Dan GO player, whom at this time is the only human to ever beat AI at a video game, which has since become somewhat of an impossible task, describing this feat as a seminal moment and one which changed the course of not only deep learning but also reinforcement learning, increasing the social belief in the subsection of AI. Since then, of course, we have seen video games and tactically based games, including Starcraft become imperative in the development of AI. The comparison of Reinforcement Learning to Human Learning is something which we often come across, referenced by Lex as something which needed addressing, with humans seemingly learning through "very few examples" as opposed to the heavy data sets needed in AI, but why is that?
Managing these destructive impacts in a sea is an arduous task. Technologies were developed and are being developed to monitor the marine ecosystem. In recent years, the use of digital cameras, Unmanned Underwater Vehicles (UUV), and Autonomous Underwater Vehicles (AUV) has led to an exponential increase in the availability of underwater imagery. reason to use … How Can deep learning help in the Marine ecosystem? Read More »