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Artificial Intelligence and Machine Learning – Path to Intelligent Automation

#artificialintelligence

With evolving technologies, intelligent automation has become a top priority for many executives in 2020. Forrester predicts the industry will continue to grow from $250 million in 2016 to $12 billion in 2023. With more companies identifying and implementation the Artificial Intelligence (AI) and Machine Learning (ML), there is seen a gradual reshaping of the enterprise. Industries across the globe integrate AI and ML with businesses to enable swift changes to key processes like marketing, customer relationships and management, product development, production and distribution, quality check, order fulfilment, resource management, and much more. AI includes a wide range of technologies such as machine learning, deep learning (DL), optical character recognition (OCR), natural language processing (NLP), voice recognition, and so on, which creates intelligent automation for organizations across multiple industrial domains when combined with robotics.


A new way to train AI systems could keep them safer from hackers

#artificialintelligence

The context: One of the best unsolved defects of deep knowing is its vulnerability to so-called adversarial attacks. When included to the input of an AI system, these perturbations, apparently random or undetected to the human eye, can make things go totally awry. Stickers tactically put on a stop indication, for instance, can deceive a self-driving automobile into seeing a speed limitation indication for 45 miles per hour, while sticker labels on a roadway can puzzle a Tesla into drifting into the incorrect lane. Safety important: Most adversarial research study concentrates on image acknowledgment systems, however deep-learning-based image restoration systems are susceptible too. This is especially uncomfortable in healthcare, where the latter are typically utilized to rebuild medical images like CT or MRI scans from x-ray information.


How AI is Changing the Mobility Landscape - DATAVERSITY

#artificialintelligence

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.


Inside the lab where Waymo is building the brains for its driverless cars

#artificialintelligence

Right now, a minivan with no one behind the steering wheel is driving through a suburb of Phoenix, Arizona. And while that may seem alarming, the company that built the "brain" powering the car's autonomy wants to assure you that it's totally safe. Waymo, the self-driving unit of Alphabet, is the only company in the world to have fully driverless vehicles on public roads today. That was made possible by a sophisticated set of neural networks powered by machine learning about which very is little is known -- until now. For the first time, Waymo is lifting the curtain on what is arguably the most important (and most difficult-to-understand) piece of its technology stack. The company, which is ahead in the self-driving car race by most metrics, confidently asserts that its cars have the most advanced brains on the road today. Anyone can buy a bunch of cameras and LIDAR sensors, slap them on a car, and call it autonomous. But training a self-driving car to behave like a human driver, or, more importantly, to drive better than a human, is on the bleeding edge of artificial intelligence research.


The Role Of Human Judgment As A Presumed Integral Ingredient For Achieving True AI

#artificialintelligence

Is human judgment the crucial missing link needed to achieve true AI? Is the embodiment of human judgment a required ingredient in achieving true AI? It is a rather seemingly simple question to proffer, though any mindful answer is likely to be notably elongated. Slightly restating the question, in order for AI to become a vaunted version of AI, which let's say we might all collegially agree is demarked as the equivalent of human-like intelligence, this weighty question is asking whether there needs to be some means to encompass or include what we variously describe or denote as "human judgment" for AI to be true AI. If you say that yes, of course, the only true AI is the type of AI that showcases its own variant of human judgment, you are then putting forth a challenge and a quest to figure out what human judgment portends and how to somehow get that thing or capability into AI systems.


Alphabet's Next Billion-Dollar Business: 10 Industries To Watch - CB Insights Research

#artificialintelligence

Alphabet is using its dominance in the search and advertising spaces -- and its massive size -- to find its next billion-dollar business. From healthcare to smart cities to banking, here are 10 industries the tech giant is targeting. With growing threats from its big tech peers Microsoft, Apple, and Amazon, Alphabet's drive to disrupt has become more urgent than ever before. The conglomerate is leveraging the power of its first moats -- search and advertising -- and its massive scale to find its next billion-dollar businesses. To protect its current profits and grow more broadly, Alphabet is edging its way into industries adjacent to the ones where it has already found success and entering new spaces entirely to find opportunities for disruption. Evidence of Alphabet's efforts is showing up in several major industries. For example, the company is using artificial intelligence to understand the causes of diseases like diabetes and cancer and how to treat them. Those learnings feed into community health projects that serve the public, and also help Alphabet's effort to build smart cities. Elsewhere, Alphabet is using its scale to build a better virtual assistant and own the consumer electronics software layer. It's also leveraging that scale to build a new kind of Google Pay-operated checking account. In this report, we examine how Alphabet and its subsidiaries are currently working to disrupt 10 major industries -- from electronics to healthcare to transportation to banking -- and what else might be on the horizon. Within the world of consumer electronics, Alphabet has already found dominance with one product: Android. Mobile operating system market share globally is controlled by the Linux-based OS that Google acquired in 2005 to fend off Microsoft and Windows Mobile. Today, however, Alphabet's consumer electronics strategy is being driven by its work in artificial intelligence. Google is building some of its own hardware under the Made by Google line -- including the Pixel smartphone, the Chromebook, and the Google Home -- but the company is doing more important work on hardware-agnostic software products like Google Assistant (which is even available on iOS).


Overview of Tools Supporting Planning for Automated Driving

arXiv.org Artificial Intelligence

Planning is an essential topic in the realm of automated driving. Besides planning algorithms that are widely covered in the literature, planning requires different software tools for its development, validation, and execution. This paper presents a survey of such tools including map representations, communication, traffic rules, open-source planning stacks and middleware, simulation, and visualization tools as well as benchmarks. We start by defining the planning task and different supporting tools. Next, we provide a comprehensive review of state-of-the-art developments and analysis of relations among them. Finally, we discuss the current gaps and suggest future research directions.


Don't trust AI until we build systems that earn trust

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To judge from the hype, artificial intelligence is inches away from ripping through the economy and destroying everyone's jobs--save for the AI scientists who build the technology and the baristas and yoga instructors who minister to them. But one critic of that view comes from within the tent of AI itself: Gary Marcus. From an academic background in psychology and neuroscience--rather than computer science--Mr Marcus has long been an AI gadfly. He relishes poking holes in the popular AI technique of deep-learning because of its inability to perform abstractions even as it does an impressive job at pattern-matching. Yet his unease with the state of the art didn't prevent him from advancing the art with his own AI startup, Geometric Intelligence, which he sold to Uber in 2016.


Don't trust AI until we build systems that earn trust

#artificialintelligence

To judge from the hype, artificial intelligence is inches away from ripping through the economy and destroying everyone's jobs--save for the AI scientists who build the technology and the baristas and yoga instructors who minister to them. But one critic of that view comes from within the tent of AI itself: Gary Marcus. From an academic background in psychology and neuroscience--rather than computer science--Mr Marcus has long been an AI gadfly. He relishes poking holes in the popular AI technique of deep-learning because of its inability to perform abstractions even as it does an impressive job at pattern-matching. Yet his unease with the state of the art didn't prevent him from advancing the art with his own AI startup, Geometric Intelligence, which he sold to Uber in 2016.


Wayve raises $20 million to give autonomous cars better AI brains

#artificialintelligence

Wayve, a U.K.-based startup that's developing artificial intelligence (AI) that teaches cars to drive autonomously using reinforcement learning, simulation, and computer vision, has raised $20 million in a series A round of funding led by Palo Alto venture capital (VC) firm Eclipse Ventures, with participation from Balderton Capital, Compound Ventures, Fly Ventures, and First Minute Capital. Several notable angel investors also participated in the round, including Uber's chief scientist Zoubin Ghahramani and Pieter Abbeel, a UC Berkeley robotics professor and pioneer of deep reinforcement learning. Founded out of Cambridge, U.K., in 2017, Wayve's core premise is that the big breakthrough in self-driving cars will come from better AI brains rather than more sensors or "hand-coded" rules. The company said that it trains its autonomous driving system using simulated environments and then transfers that knowledge into the real world, where it emulates how humans adapt to conditions in real time. Wayve's systems learn from each safety driver intervention to understand why the driver had to intervene, bypassing HD maps, lidar, and other sensors that have become synonymous with the burgeoning autonomous vehicle movement.