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) …
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China's Communist Government has extracted over 6 billion peoples biometrics, including facial, voice and personal health data to empower their Quantum Artificial Intelligence program meant for military purposes. This includes almost every American, Canadian, and European persons living today, every person in China, and Less so from groups in Africa, the Middle East, and South America. I initially made the finding public by publishing the discovery in the book AI, Trump, China and the Weaponization of Robotics without providing company names. Later, I included the findings with company names in the updated book Artificial Intelligence Dangers to Humanity. More than 1,000 AI, Robotics and Bio-Metric companies were researched to obtain the results of over 6 billion human beings who have had their bio-metrics stolen or transferred to China.
On Tinder, an opening line can go south pretty quickly. And while there are plenty of Instagram accounts dedicated to exposing these "Tinder nightmares," when the company looked at its numbers, it found that users reported only a fraction of behavior that violated its community standards. Now, Tinder is turning to artificial intelligence to help people dealing with grossness in the DMs. The popular online dating app will use machine learning to automatically screen for potentially offensive messages. If a message gets flagged in the system, Tinder will ask its recipient: "Does this bother you?"
Medical robots have demonstrated the ability to manipulate percutaneous instruments into soft tissue anatomy while working beyond the limits of human perception and dexterity. Robotic technologies further offer the promise of autonomy in carrying out critical tasks with minimal supervision when resources are limited. Here, we present a portable robotic device capable of introducing needles and catheters into deformable tissues such as blood vessels to draw blood or deliver fluids autonomously. Robotic cannulation is driven by predictions from a series of deep convolutional neural networks that encode spatiotemporal information from multimodal image sequences to guide real-time servoing. We demonstrate, through imaging and robotic tracking studies in volunteers, the ability of the device to segment, classify, localize and track peripheral vessels in the presence of anatomical variability and motion.
Every year, hundreds of thousands of people die in accidents involving motor vehicles. Recently, an AI startup called VizibleZone devised a method to possibly prevent some of these deaths. As reported by VentureBeat, VizibleZone's AI-powered system integrates data collected by both motor vehicles and smartphones in order to alert drivers to the potential location of a pedestrian, which can help drivers avoid tragic accidents. According to the World Health Organization, in 2018 around 1.5 million people were killed in road accidents. More than half of all of the deaths associated with these accidents involved a collision between a pedestrian or cyclist and a motor vehicle.
Last week I had the privilege to serve as a panelist at a Northwestern Pritzker School of Law event entitled "Symposium 2020: AI, the New Law Firm Attorney: Artificial Intelligence Entering the Legal Profession." The event's keynote speaker Seyfarth Shaw Chair Emeritus Stephen Poor and our panel explored the growing impact of Artificial Intelligence (AI) tools in the legal industry to help lawyers achieve more by getting out of the repetitive, routine and mundane tasks that lawyers have performed in the past so they can "practice at the top of their license" as Mr. Poor stated. I love the phrase "practice at the top of their license" as all lawyers will need to do more of this – especially as we see the rise of tech intensity as technology plays a bigger role in our professional and personal lives and leading technology like AI is increasingly used by lawyers, law firms and other legal organizations to deliver legal services to their clients. As AI tools seek to automate and perform certain tasks that have been traditionally performed by lawyers, I believe that the Emotional Intelligence or Emotional Quotient (EQ) skills that lawyers use everyday to deliver legal services to their clients will be more important than ever before as stronger EQ skills will help enable lawyers to truly "practice at the top of their license." Since AI, algorithms, machines and technology do not embrace EQ, the proverbial "soft skills" that are often associated with EQ can help lawyers provide even more high-impact/high-value legal counsel to their clients and differentiate their legal services from others.
The General Services Administration expects that its new partnership with the Pentagon's Joint Artificial Intelligence Center will ultimately lead to significant benefits for civilian agencies. The GSA is working with JAIC, which was established last year to speed up AI adoption across the Pentagon, to accelerate the center's process by adding AI into acquisition work, which GSA officials said they hope to turn around and offer civilian government. "We're able to utilize a lot of that educational material [and] best practices that they're getting and scale it up, standardize it in a sense so it can be spread among civilian agencies," said Omid Ghaffari-Tabrizi, acquisition lead at the GSA Centers of Excellence, speaking Dec. 5 at the GovernmentCIO AI and RPA in Government conference. "All of the AI that we're procuring for them, we're also hoping to procure for ourselves," Ghaffari-Tabrizi added. One frustration with the acquisition process is the time it takes from the start of the project to the end.
Unbeknownst to the CEO of a company who was interviewed on TV last year, a hacking group that was trailing the CEO taped the interview and then taught a computer to perfectly imitate the CEO's voice -- so it could then give credible instructions for a wire transfer of funds to a third party. This "voice phishing" hack brought to light the growing abilities of artificial intelligence-based technologies to perpetuate cyber-attacks and cyber-crime. Using new AI-based software, hackers have imitated the voices of a number of senior company officials around the world and thereby given out instructions to perform transactions for them, such as money transfers. The software can learn how to perfectly imitate a voice after just 20 minutes of listening to it and can then speak with that voice and say things that the hacker types into the software. Get The Start-Up Israel's Daily Start-Up by email and never miss our top stories Free Sign Up Some of these attempts were foiled, but other hackers were successful in getting their hands on money.
I recently asked the Twitter community about their biggest machine learning pain points and what work their teams plan to focus on in 2020. One of the most frequently mentioned pain points was deploying machine learning models. More specifically, "How do you deploy machine learning models in an automated, reproducible, and auditable manner?" The topic of ML deployment is rarely discussed when machine learning is taught. Boot camps, data science graduate programs, and online courses tend to focus on training algorithms and neural network architectures because these are "core" machine learning ideas.