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) …
Setting up a profile on most dating apps is simple. You input your name, upload some photos, set your location and sexual preferences and you're launched into a sea of mostly singles to chat with, meet and take things from there. During the process, you're also giving up valuable, personal information to platforms that often monetize by selling that data to third parties you've never heard of. Not to mention, data breaches abound. Grindr, OkCupid and Tinder, for example, were at the center of controversy last week when researchers accused the companies of disclosing highly personal information and breaking privacy laws.
Hundreds of law enforcement agencies across the US have started using a new facial recognition system from Clearview AI, a new investigation by The New York Times has revealed. The database is made up of billions of images scraped from millions of sites including Facebook, YouTube, and Venmo. The Times says that Clearview AI's work could "end privacy as we know it," and the piece is well worth a read in its entirety. The use of facial recognition systems by police is already a growing concern, but the scale of Clearview AI's database, not to mention the methods it used to assemble it, is particularly troubling. The Clearview system is built upon a database of over three billion images scraped from the internet, a process which may have violated websites' terms of service.
The Company announced the formation of new online social group to bring together programmers and A.I. enthusiasts from the US, India, and across the globe. Data Elf online social groups geared towards the data science and programming community from individuals interested in Python programming, advanced Machine learning, Robotics, or Enterprise Solutions. One of biggest issues for new data programmers is finding ways to turn their newly formed skills into new job opportunities. Data Elf Programmer Profiles allow developers to list their programming and development skills to global audience in regards for employment or contract work. To view programmer profiles please visit https://dataelf.com/list/
Using one of your examples, for me the symbolic constraints for OCR of printed source code seem conceptually similar to how standard OCR systems implement the split between the character OCR model (which provides the probabilities of individual characters) and the language model (which provides the probabilities of long combined sequences of possible alternative characters) - any symbolic rules and constraints could be integrated straight into the language model, by adding an extra penalty to the likelihood of sequences that don't match some rule. The algorithms to effectively explore the solution space in this manner (often some form of beam search) already exist and would be already implemented and tested in an OCR system, so the symbolic rules would just change how the cost function is calculated.
Quantum computing and Artificial Intelligence (AI) both have one thing in common: they inspire us humans to dream of a new computing paradigm that entirely replaces the status quo. Something that brings computing to a whole new level where quantum supplies unlimited compute speed and capacity while AI takes care of automating all operational tasks and prepares optimal (human) decision making. Fancy case studies about "quantum supremacy" and AI learning how to open a door, drive a car, or play chess from scratch seem to confirm our excitement around these technologies. In times where most individuals and enterprises are still struggling with accessing, integrating, and managing data to optimize our daily decision making, we are fascinated by what could be if we only could get our hands on "true" AI and quantum. We want AI today to inform complex strategic decisions, to observe our own actions and their outcomes so that it can learn what works and what does not, and obviously, to point out where its own knowledge gaps are that we can help it fill.
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A Utah city police department is considering a partnership with an artificial intelligence company in an effort to help the law enforcement agency work more efficiently. The Springville police may work with technology firm Banjo to help improve the response time to emergencies, The Daily Herald reported. The Park City company can gather real-time data from various sources including 911 dispatch calls, traffic cameras, emergency alarms, and social media posts and report related information to the police, officials said. The Springfield City Council heard a presentation by a Banjo representative during its Jan. 7 meeting but did not immediately make a decision about using the technology. Banjo entered an agreement last July with the Utah Attorney General's Office and the Utah Department of Public Safety to let the agencies use Banjo's technology to "reduce time and resources typically required to generate leads, and instead focus their efforts on incident response," according to a report to the state Legislature.
In my experience leading transformation initiatives, I have seen many similarities between broader business transformations and AI transformations, but there is one key difference – in AI, the world is still highly experimental. In fact, 3 out of 4 of AI projects fail to show positive returns on investment. Because of this, there hasn't been a map of what AI success at scale really looks like. It remains uncharted territory for enterprises. In October 2019, we commissioned a study with Inc.digital, where we talked to 550 executives across a variety of industries to understand why enterprises don't more frequently see tangible, measurable results from AI. Through this research, we were able to determine the DNA of the few that are successful with their AI strategy – the one common thread is keeping the AI strategy, data, technology, people and processes close to the core and controlled.
To stop spread of disinformation leading to widespread public disorder, the government is exploring use of Artificial Intelligence (AI) to remove such content automatically from social media platforms. The Centre is to "take up the issue with representatives of various international social media platforms operating in the country and monitor their compliance to instructions issued by lawful authorities under Information Technology Act." Sources said, "The introduction of Artificial Intelligence to remove objectionable content automatically from social media platforms needs to be explored." This step was proposed after the government witnessed widespread public disorder because of spread of rumours in mob lynching cases. The Ministry of Home Affairs has taken up the matter and is exploring ways to implement it. On the rise in sharing of fake news over social media platforms such as Facebook, Twitter and WhatsApp, Minister of Electronics and Information Technology Ravi Shankar Prasad had said in Lok Sabha that "With a borderless cyberspace coupled with the possibility of instant communication and anonymity, the potential for misuse of cyberspace and social media platforms for criminal activities is a global issue."