When Elon Musk talks about the future of factory automation at Tesla, he envisions new breeds of robots and smart machines compressed in dense factories with little room for human operators, guided by self-learning software. "At the point at which the factory looks like an "alien dreadnought" -- a nod to a video game spaceship -- "you know you've won," Musk has told investors. But so far, the manufacturing of Tesla's new all-electric compact sedan, the Model 3, at its Fremont, Calif., factory is moving at a more earthbound pace. When Musk launched the car at an elaborate stage show in July, Tesla was anticipating a production rate of 20,000 Model 3s a month by the end of December. Over three months through September, though, Tesla had produced only 260 Model 3s -- about three cars a day.
In May, Sundar Pichai, CEO of Google, discussed AI applications for digital pathology in his keynote speech to an audience of millions at Google's annual I/O event. Five weeks earlier, the FDA announced it had approved the first whole slide imaging system for primary diagnostic use in pathology. Both events point to the future of pathology and laboratory medicine: Software will soon dominate. Over the past 20 years, software has taken over the world. Retail was dominated by Amazon, Netflix put Blockbuster out of business, and Uber used software to take over the taxi industry.
Artificial intelligence (AI) is set to substantially disrupt the financial services industry, transforming how we bank, invest, and get insured. AI refers to machines that are capable of performing specific tasks that normally require human intelligence such as visual perception, speech recognition, decision-making, and language translation. AI and related technologies are made possible by the colossal volumes of data we are able to collect and process. AI has been all the buzz these past few years, and according to CB Insights, AI startups raised over US$2 billion in 2016 alone. In the area of financial services, AI is expected to bring major shifts in financial institutions' workforces.
The computer that stunned humanity by beating the best mortal players at a strategy board game requiring "intuition" has become even smarter, its creators claim. Even more startling, the updated version of AlphaGo is entirely self-taught -- a major step towards the rise of machines that achieve superhuman abilities "with no human input", they reported in the science journal Nature. Dubbed AlphaGo Zero, the Artificial Intelligence (AI) system learnt by itself, within days, to master the ancient Chinese board game known as "Go" -- said to be the most complex two-person challenge ever invented. It came up with its own, novel moves to eclipse all the Go acumen humans have acquired over thousands of years. After just three days of self-training it was put to the ultimate test against AlphaGo, its forerunner which previously dethroned the top human champs.
Boeing's venture capital unit HorizonX is continuing its investment in autonomous technologies, recently backing Near Earth Autonomy, a Pittsburgh-based company that develops technologies to enable safe and reliable autonomous flights. The aerospace giant announced the investment on Thursday, but did not disclose the amount it has invested in the company. Near Earth Autonomy, which was spun out of Carnegie Mellon University's Robotics Institute, develops software and sensor technology for three-dimensional mapping and survey, motion planning, and landing zone assessment, among others. Its products are aimed at enabling aircraft to operate autonomously. According to a Washington Post report, the company has developed self-piloting surveillance drones that can navigate underground pathways, and is exploring ways for autonomous planes to navigate without reliance on GPS satellites.
"We think this could be the third wave where you have programmable objects blanketing your home," said David Eun, president of Samsung NEXT, Samsung's investment group, during an interview at The Wall Street Journal's WSJ D. Live technology conference. Companies across tech have been rushing to launch products and software for the so-called smart home. Inc.'s Alexa and Alphabet Inc.'s Google Assistant have made it possible to embed artificial intelligence in everyday home devices, letting people unlock doors and dim lights with their voices. Those companies and Apple Inc. are launching smart speakers, as well. Samsung has an inherent hardware advantage in this arena because it sells an array of appliances.
Artificial intelligence has conquered games and image recognition, but will it master investing? The short answer is yes, but how soon and how complete? Machine learning methods have had impressive recent successes. These include defeating humans at chess, Jeopardy, poker and Go, as well as providing superior image and speech recognition. Developers strive to create tools that automate decision making and that can mimic or exceed human performance for specific tasks.
The number jobs in artificial intelligence (AI) in the UK has risen dramatically in the last three years, according to Indeed. Since 2014, the number of available AI roles in Britain has increased by 485% - representing a significant spike in demand for employees with the appropriate skills for the job. Yet Indeed's data also reveals there are over two times as many AI jobs available than there are suitable applicants, with a ratio of 2.3 roles available per candidate searching in the last quarter. Interest in AI roles has risen more steadily by 178% in the past three and a half years, not quite high enough to meet the fivefold surge in postings. The popularity of software in innovations including smart home devices and customer service chat bots demonstrate how the industry is developing at pace.
Since the 2013 Target breach, it's been clear that companies need to respond better to security alerts even as volumes have gone up. With this year's fast-spreading ransomware attacks and ever-tightening compliance requirements, response must be much faster. Adding staff is tough with the cybersecurity hiring crunch, so companies are turning to machine learning and artificial intelligence (AI) to automate tasks and better detect bad behavior. In a cybersecurity context, AI is software that perceives its environment well enough to identify events and take action against a predefined purpose. AI is particularly good at recognizing patterns and anomalies within them, which makes it an excellent tool to detect threats.
With the revolution of machine learning, a new trend has come to town and it is called face recognition. When Apple announced its FaceID feature, everyone started talking and thinking about implementing face recognition everywhere – business, mobile apps, medicine, retail, and whatnot. But how can you be sure this technology is what you need without its thorough understanding? We'll tell you today what face recognition is, how it works, and what are the different use cases for this technology. Let's just say, after reading this article, you'll become a real Jedi of face recognition.