The U.S. is using every tool at its disposal to defeat the novel coronavirus, including artificial intelligence. American laboratories are harnessing AI to discover new therapeutics. The Food and Drug Administration approved an AI tool to help detect coronavirus in CT scans. And the White House led an initiative to create a database with more than 128,000 articles that scientists can analyze using AI to help understand the virus better and develop treatments.
Apple has acquired the machine learning startup Inductiv Inc., according to a new report from Bloomberg. The startup had been developing technology that uses artificial intelligence to identify and correct errors in datasets. The report explains that the engineering team from Inductiv has joined Apple "in recent weeks" to work on several different projects including Siri, machine learning, and data science. Apple issued its standard statement regarding the acquisition, saying it "buys smaller technology companies from time to time and we generally do not discuss our purpose or plans." The startup was founded by professors from Stanford University, the University of Waterloo, and the University of Wisconsin.
A business operation hard hit by COVID-19 is the call center. Industries ranging from airlines to retailers to financial institutions have been bombarded with calls--forcing them to put customers on hold for hours at a time or send them straight to voicemail. A recent study from Tethr of roughly 1 million customer service calls showed that in just two weeks, companies saw the percentage of calls scored as "difficult" double from 10 percent to more than 20 percent. Issues stemming from COVID-19--such as travel cancellations and gym membership disputes--have also raised customer anxiety, making call center representatives' jobs that much more challenging. Companies thinking about investing in speech recognition should consider a deep learning-based approach, and what to take into consideration before implementing it.
The "Sensors for Robotics: Technologies and Global Markets" report has been added to ResearchAndMarkets.com's offering This report sizes the market by technology, including sensors within the vision, touch, hearing, and movement segments. The top seven application areas are sized, forecast, and discussed in-depth. These include agriculture, appliances, automotive, healthcare, industrial, logistics, and military. In addition, the overall market and each application area are assessed on a worldwide and regional basis, including North America, Latin America, Europe, the Middle East and Africa, and Asia-Pacific. This report considers the economic slowdown caused by lockdown across the world owing to the COVID-19 pandemic.
Mathematical intuition required for Data Science and Machine Learning. The linear algebra intuition required to become a Data Scientist. Then, this course is for you. The Common mistake by a data scientist is Applying the tools without the intuition of how it works and behaves. Having the solid foundation of mathematics will help you to understand how each algorithms work, its limitations and its underlying assumptions.
Learn to create Deep Learning Algorithms in Python from two Machine Learning & Data Science experts. Artificial intelligence is growing exponentially. There is no doubt about that. Self-driving cars are clocking up millions of miles, IBM Watson is diagnosing patients better than armies of doctors and Google Deepmind's AlphaGo beat the World champion at Go - a game where intuition plays a key role. But the further AI advances, the more complex become the problems it needs to solve.
According to Wikipedia, apophenia is "the tendency to mistakenly perceive connections and meaning between unrelated things" . It is also used as "the human propensity to seek patterns in random information". Whether it's a scientist doing research in a lab, or a conspiracy theorist warning us about how "it's all connected", I guess people need to feel like we understand what's going on, even in the face of clearly random information. Deep Neural Networks are usually treated like "black boxes" due to their inscrutability compared to more transparent models, like XGboost or Explainable Boosted Machines. However, there is a way to interpret what each individual filter is doing in a Convolutional Neural Network, and which kinds of images it is learning to detect.
Contrary to popular belief, machine learning has been around for several decades. It was initially shunned due to its large computational requirements and the limitations of computing power present at the time. However, machine learning has seen a revival in recent years due to the preponderance of data stemming from the information explosion. So, if machine learning and statistics are synonymous with one another, why are we not seeing every statistics department in every university closing down or transitioning to being a'machine learning' department? Because they are not the same!
If your work puts you in regular contact with technology vendors, you'll have heard terms such as artificial intelligence (AI), machine learning (ML), natural language processing and computer vision before. You'll have heard that AI/ML is the future, that the boundaries of these technologies are constantly being pushed and broadened, and that AI/ML will play an integral role in shaping this tech-forward era's most successful business models. As a technology leader, I've heard all these claims and more. To say that AI/ML will play an increasingly impactful role in business is no overstatement. According to a recent Forbes article, the machine learning market is poised to more than quadruple in the coming years.
"I would say everyone has read at least once an algorithmically produced article," said Robert Weissgraeber, CTO and Managing Director of AX Semantics. In many cases, readers don't see a difference between human- and bot-authored copy, Weissgraeber told Built In. His company, AX Semantics, is one of several -- including Narrative Science and Automated Insights -- exploring natural language generation, or automated writing. The technology can be used to generate product descriptions, quarterly earnings reports, fantasy football recaps and journalism. The Washington Post, for instance, has developed an AI-enabled bot, Heliograf, that helps generate election and sports coverage.