Not many robotics companies can boast legions of fans online, but not many robotics companies make robots quite like Boston Dynamics. Each time the firm shares new footage of its machines, they cause a sensation. Whether it's a pack of robot dogs towing a truck or a human-like bot leaping nimbly up a set of boxes, Boston Dynamics' bots are uniquely thrilling. And when a parody video circulated last month showing a CGI "Bosstown Dynamics" robot turning on its creators, many mistook it for the real thing -- a testament to how far the company has pushed what seems technologically possible. But for all its engineering prowess, Boston Dynamics now faces its biggest challenge yet: turning its stable of robots into an actual business.
Artificial intelligence (AI) is rapidly finding applications in nearly every walk of life. Self-driving cars, social media networks, cybersecurity companies, and everything in between uses it. But a new report published by the SHERPA consortium – an EU project studying the impact of AI on ethics and human rights – finds that while human attackers have access to machine learning techniques, they currently focus most of their efforts on manipulating existing AI systems for malicious purposes instead of creating new attacks that would use machine learning. The study's primary focus is on how malicious actors can abuse AI, machine learning, and smart information systems. The researchers identify a variety of potentially malicious uses for AI that are well within reach of today's attackers, including the creation of sophisticated disinformation and social engineering campaigns.
As the amount of data continues to grow at an almost incomprehensible rate, being able to understand and process data is becoming a key differentiator for competitive organizations. Machine learning applications are everywhere, from self-driving cars, spam detection, document search, and trading strategies, to speech recognition. This makes machine learning well-suited to the present-day era of Big Data and Data Science. The main challenge is how to transform data into actionable knowledge. Machine Learning in Java will provide you with the techniques and tools you need to quickly gain insight from complex data.
This AI, meanwhile, simulates day from a night picture. This is valuable, as creating self-driving cars that work and can locate themselves precisely in all conditions - day, night, fog, rain, snow and so on - requires a lot of data that covers all scenarios. Collecting large amounts of data in all conditions is practically very difficult, as certain conditions (such as snow) occur very rarely in some areas. Instead of collecting more data, scientists have come up with this night-to-day workaround. This could also lead to better night vision for the military, airplane pilots and human drivers.
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About the author Andrew Macleod is the director of automotive marketing at Siemens, focusing on the Mentor product suite. He has more than 15 years of experience in the automotive software and semiconductor industry, with expertise in new product development and introduction, automotive integrated circuit product management and global strategy, including a focus on the Chinese auto industry. He earned a 1st class honors engineering degree from the University of Paisley in the UK and lives in Austin, Texas.
Recent studies demonstrate that machine learning algorithms can discriminate based on classes like race and gender. In this work, we present an approach to evaluate bias present in automated facial analysis algorithms and datasets with respect to phenotypic subgroups. Using the dermatologist approved Fitzpatrick Skin Type classification system, we characterize the gender and skin type distribution of two facial analysis benchmarks, IJB-A and Adience. We find that these datasets are overwhelmingly composed of lighter-skinned subjects (79.6% for IJB-A and 86.2% for Adience) and introduce a new facial analysis dataset which is balanced by gender and skin type. We evaluate 3 commercial gender classification systems using our dataset and show that darker-skinned females are the most misclassified group (with error rates of up to 34.7%).
Do chatbots, robots and digital assistants intrigue you? What about automated vehicles and virtual assistants? AI is a field of computer science that focuses on the creation of a machine that can replicate human behaviour. The science fiction of yesterday quickly becomes reality. AI statistics surrounding the business and tech industries are changing.
Uber AI is the research and platform team for everything AI at the company with the exception of self-driving cars. Self-driving cars are left to Uber ATG. Ludwig allows you to specify a Tensorflow model in a declarative format that focuses on your inputs and outputs. Ludwig then builds a model that can deal with those types of inputs and outputs without a developer explicitly specifying how that is done. Because of Ludwig's datatype abstraction for inputs and outputs, there is a huge range of applications that can be created.
Smartphones, smart speakers, smart cars, smart coffee makers...the list goes on. It seems like everything around us is coming to life and becoming intelligent. And though the sci-fi genre thrives on our ever-present fear of a hostile robot takeover, smart devices are anything but dystopian -- they're actually here to make our lives easier so we can spend more time on the important stuff instead of tedious busywork. Tech companies know that increased automation is the way of the future, just like it was when Ford pioneered the assembly line. Advanced technology like artificial intelligence (AI) and machine learning (ML) is fueling the most exciting innovations in recent history -- think self-driving cars, virtual and augmented reality, automated investing, improved medical imaging, and more.