If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
StradVision has raised $16.6M in total. We talked with Junhwan Kim, its CEO. How would you describe StradVision in a single tweet? StradVision is a pioneer in deep learning-based vision processing technology, providing the software that will allow Advanced Driver-Assistance Aystems (ADAS) in autonomous vehicles to reach the next level of safety, and usher in the era of the fully autonomous vehicle. How did it all start and why?
Academic machine learning involves almost exclusively offline evaluation of machine learning models. In the real world this is, somewhat surprisingly, often only good enough for a rough cut that eliminates the real dogs. For production work, online evaluation is often the only option to determine which of several final-round candidates might be chosen for further use. As Einstein is rumored to have said, theory and practice are the same, in theory. So it is with models.
Natural language processing (NLP) is a branch of artificial intelligence. It helps computers understand, interpret and manipulate human text language. Today there are an enormous amount of emails, social media text, video stream, customer reviews, customer support requests, etc. All of these textual data become the perfect place to apply NLP. We need NLP tools and techniques to process, analyze, and understand unstructured "big data" in order to release the power in analytics.
For many organizations, automating hundreds or thousands of manual tasks has become a competitive necessity. However the same attributes that make automation so effective also open up organizations to new risks. The reason: Because AI enables a company to remotely manage machines and make them interact with other machines, bad actors could take control of the technology and wreak havoc. Consider the common threats to a computer network: malignant software such as viruses and phishing emails for tricking people into revealing valuable information. If a worker clicks on a malware link and damages his computer on the network, the virus can spread to others.
The rise of artificial intelligence has brought with it a strange paradox: Although AI itself is grounded in data and logic, business users can be tempted to throw rationality out the window when dealing with AI models. After all, AI algorithms have been imbued with nearly magical properties, capable of telling a business how to identify risky customers, predict customer behavior, structure the perfect incentive program to reduce customer churn, and just generally provide elegant, unbiased answers to a business's most vexing questions. If only we could put our trust in what we wish were pure, scientifically derived AI models. Alas, it is more complicated than that. In this article, I will describe two of the major pitfalls associated with artificial intelligence models, discuss why it can be so difficult to avoid these pitfalls, and offer some ideas on how to move past them to make AI more useful and profitable.
The machine learning community has witnessed a surge in releases of frameworks, libraries and software. Tech pioneers like Google, Amazon, Microsoft and others have insisted their intention behind open-sourcing their technology. However, there has been a growing trend of these tech giants claiming ownership for their innovations. According to the National Bureau of Economic Research study, in 2010, there were 145 US patent filings that mentioned machine learning, compared to 594 in 2016. Google, especially, has filed patents related to machine learning and neural networks 99 times in 2016 alone.
Indonesian insurance provider TUGU Insurance has launched the "tdrive" mobile app to deliver a seamless brand experience to its agents and customers. Developed by Singapore-based insurtech company Zensung, the app leverages artificial intelligence and Internet of Things to prevent and reduce fraudulent claims. The app is currently available for TUGU Insurance's auto insurance products, but plans to expand it to other verticals such as travel, medical and home insurance are in the pipeline. While enabling agents and policyholders to buy insurance and submit their claims digitally, the app also aims to help them understand potential risks. According to a press release, tdrive includes functions to promote safe driving and drivers safety as well as carbon footprint awareness for drivers and organisations.
This is the second in a series of articles looking at the impact of artificial intelligence and emerging technologies in restaurants. Artificial intelligence is becoming a crucial asset in the restaurant technology playbook. But as many brands invest in consumer-facing products like automated drive-thrus and in-store kiosks, experts argue that back-of-house improvement will have the biggest impact on bottom lines. As the industry nears an AI tipping point, it's more important than ever for smaller companies to seriously consider how to implement the tech in their systems -- or risk facing serious consequences in three to five years, Aaron Allen & Associates CEO Aaron Allen told Restaurant Dive. "We see massive closures and bankruptcies and retooling of restaurants in much the same way we're seeing in retail," Allen said.