If we're going to map the world, we're not going to do it with ever-greater volumes of elbow grease. There's just too much work to do. AI and computer vision are helpful assistants in this task, however, as a Facebook effort has shown, laying down hundreds of thousands of miles of previously unmapped roads in Thailand and other less well-covered countries. The problem is simply that there's a whole lot of Earth and only a handful of people actually making maps of it. Sure, Google and Apple have dueling products -- but their focus is on businesses in cities and accurate navigation, not including every dirt path and gravel road.
While the United States currently has an edge in the race to develop artificial intelligence, China is rapidly gaining ground as Europe falls behind, according to a report released today by the Center for Data Innovation. The study arrives amid a wide-ranging debate about which region has gained AI leadership, and the implications that holds for dominating cutting-edge technologies such as autonomous vehicles and other forms of automation. The winners of an AI arms race could hold a significant economic advantage in the decades to come. There has been growing concern among U.S. tech companies and policymakers that China's initiative to make it dominant in AI by 2030 is allowing it to dictate this critical field. The ability of its central government to allow sweeping data gathering and determine official champions to lead this charge seems to have given its efforts significant momentum.
Artificial intelligence has promised to revolutionize our lives, taking over the mundane tasks of daily existence, from prewriting "smart" email replies to driving our car through rush hour traffic. In the PR realm, AI has been touted as equal parts something to celebrate (no more manual coverage reports!) and fear (er, so long, means of employment). But the truth, as usual, lies somewhere in between. Some form of intelligent technology is already embedded in the PR industry, from the tools we use to find new audiences and monitor evolving conversations to modern media placement. Bloomberg News uses AI to generate coverage on some 3,500 earnings reports every quarter.
Have you ever wondered whom to thank for some of the modern conveniences you might have started taking for granted, like Siri, Cortana or Alexa (assuming you agree these are conveniences)? The people at the Association for Computing Machinery (ACM) decided to thank Geoffrey Hinton, Yoshua Bengio and Yann LeCun in April of this year by honoring them with the Turing Award for their contributions to deep learning and neural networks. These contributions are put to use every time you log into your smartphone using fingerprint or facial recognition or when you use Google Photos or a voice assistant, and likely every time you use Amazon, Netflix, Facebook or Instagram. The advances in automatic language translation and autonomous cars in recent years arguably wouldn't have progressed as rapidly had it not been for the contributions of these three researchers. All of that is still an understatement of their contributions to artificial intelligence (AI).
Scale AI Inc., a three-year-old startup run by a 22-year-old, is teaching machines how to see. For that, it just joined Silicon Valley's list of unicorns with a fresh $100 million investment that puts its valuation above the coveted $1 billion mark, and its artificial intelligence (AI) technology has already attracted big-name customers in the field for autonomous vehicles, according to Bloomberg. Alphabet Inc.'s (GOOGL) Waymo, General Motor Co.'s (GM) Cruise, and Uber Technologies Inc. (UBER) are all buying what Scale has to offer, because well, self-driving cars are machines that need to be able to see. Scale stands out because it has built a set of software tools that are significantly reducing the time it takes to train a machine how to process and interpret visual imagery. And less time means lower costs.
Difficulties in explaining machine learning (ML) models is causing concern as banks look to the technology for default risk analysis, according to market participants. "Many different types of'black-box' models have been developed out there even by banks claiming that they can accurately predict mortgage defaults. This is only partially true," said Panos Skliamis, chief executive officer at SPIN Analytics in an email. "[These models] usually target a relatively short-term horizon and their validation windows of testing remain actually in an environment too similar to that of the development samples. However, mortgage loans are almost always long-term and their lives extend to multiple economic cycles, while the entire world changes over time and several features of ML models severely influenced by these changes of the environment," he said.
AI computing needs high levels of data processing and conventional AI systems function by transmitting data to a cloud server to be processed. Insights about the data and the decisions to be taken by the system are then transmitted back to connected devices. This approach works fine but for the rapidly increasing number of IoT devices, this is not ideal. There are issues both with the processing power, cloud connectivity and battery capacities in the mobile devices. While connected devices are not ideal to support large data crunching, sometimes they are designed for purposes that need insights in real-time, such as in self-driving cars or in anomaly detection systems.
Should Elon Musk's robot-surgeon start inserting electrodes into human brains to connect humans and computers via a high-bandwidth brain-machine? What exactly are the implications for medical insurance? Should a self-driving flying taxi crash and kill civilians? These are the questions our CEO, Lizé Lambrechts, is asking. The insurance industry is developing new ways to assess and underwrite risk as artificial intelligence (AI) and automation advance.
Many airports hope to start using biometric scanners in lieu of passports to identify travelers. Buzz60's Tony Spitz has the details. The next time you go to the airport you might notice something different as part of the security process: A machine scanning your face to verify your identity. U.S. Customs and Border Protection (CBP) has been working with airlines to implement biometric face scanners in domestic airports to better streamline security. But how does the process work?
Machine learning and artificial intelligence (AI) systems are rapidly being adopted across the economy and society. These AI algorithms, many of which process fast-growing datasets, are increasingly used to deliver personalised, interactive, 'smart' goods and services that affect everything from how banks provide advice to how chairs and buildings are designed. There is no doubt that AI has a huge potential to facilitate and enhance a large number of human activities and that it will provide new and exciting insights into human behaviour and cognition. The further development of AI will boost the rise of new and innovative enterprises, will result in promising new services and products in – for instance – transportation, health care, education and the home environment. They may transform, and even disrupt, the way public and private organisations currently work and the way our everyday social interactions take place.