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) …
Bias will continue to be a fundamental concern for businesses hoping to adopt artificial intelligence software, according to senior executives from IBM and Salesforce, two of the leading companies selling such A.I.-enabled tools. Companies have become increasingly wary that hidden biases in the data used to train A.I. systems may result in outcomes that unfairly--and in some cases illegally--discriminate against protected groups, such as women and minorities. For instance, some facial recognition systems have been found to be less accurate at differentiating between dark-skinned faces as opposed to lighter-skinned ones, because the data used to train such systems contained far fewer examples of dark-skinned people. In one of the most notorious examples, a system used by some state judicial systems to help decide whether to grant bail or parole was more likely to rate black prisoners as having a higher risk of re-offending than white prisoners with similar criminal records. "Bias is going to be one of the fundamental issues of A.I. in the future," Richard Socher, the chief scientist at software company Salesforce, said.
What do we mean when we say'context'? In essence, context is the information that frames something to give it meaning. Taken on its own, a shout could be anything from an expression of joy to warning. In the context of a structured piece of on-stage Grime, it's what made Stormzy's appearance at Glastonbury the triumph it was. The problem is that context doesn't come free – it has to be discovered.
Data Science is one of the best careers you could be getting into right now. Companies are hiring legions of data scientists at excellent salaries, and the work is as challenging as it is enjoyable. It's no surprise, then, that we've seen a blossoming of books, courses, and entire educational programs aimed specifically at training data scientists. But there are many people, myself included, who like to do part or all of their learning from books. Being able to re-read important sections, pause to think over a problem, and circle back around to earlier chapters combine to make for a very effective way to climb the learning curve.
They personalize every aspect of the viewing experience, right down to the thumbnail images people see on their homepages. If you've watched Kill Bill or Grease lately, Netflix's sophisticated recommendation algorithm may peg you as a fan of John Travolta or Uma Thurman. That may dictate who's pictured alongside your Pulp Fiction recommendation. That's just one example of a company using artificial intelligence to drive business results; relevant recommendations increases engagement and saves Netflix $1 billion each year. McKinsey reports that retailers who embrace AI just as wholeheartedly generate profit margins 10 percentage points higher than those who do not.
Thousands of engineers write the code to create our apps, which serve billions of people worldwide. This is no trivial task--our services have grown so diverse and complex that the codebase contains millions of lines of code that intersect with a wide variety of different systems, from messaging to image rendering. To simplify and speed the process of writing code that will make an impact on so many systems, engineers often want a way to find how someone else has handled a similar task. We created Aroma, a code-to-code search and recommendation tool that uses machine learning (ML) to make the process of gaining insights from big codebases much easier. Prior to Aroma, none of the existing tools fully addressed this problem.
Knowing when and where a person is, was, and will be can enable magical customer experiences. Flybits today announced that it's raised $35 million in series C funding led by Point72 Ventures, with participation from Mastercard, Citi Ventures, and Reinventure, along with existing partners Portag3 Ventures, TD Bank, and Information Venture Partners. The fresh funding brings its total raised to $50 million, and it comes as Flybits notches 300% growth in 2019 and gears up to hire across sales, engineering, and business development teams and offices, including adding solutions engineers, sales executives, business development reps, and engineers. "Customers are already used to seeing content and recommendations based on their behavior," said CEO Hossein Rahnama. But Flybits leverages an unlimited amount to create far more personalized and relevant recommendations than ever before, all in an effort to help financial institutions deliver real time lifestyle banking that gets at their customers' deeper needs.
Machine learning is the hot buzzword of the last couple years. While some applications have been a little strange, it's likely that machine learning can make shopping a little easier by inferring a shopper's interests based on their history with the store. Valve unveiled Steam Labs to share its machine learning results with the world. Two of the Steam Labs experiments revolve around video content. The Micro Trailers experiment employs deep learning to automatically trim official game trailers into six-second video clips.
By now, most of us are comfortable with the idea that artificial intelligence (AI) is here to stay and that it holds enormous potential to change the way the world works. However, to the average person, the conversation can seem dominated by visions of the future, with experts keen to discuss what AI will do, rather than what it does right now. AI is making a practical impact as we speak, and for industries such as retail, it is already forming a fundamental part of a business strategy. AI learns from data and in particular, it learns how to predict. These predictions can be used in many applications and then integrated into business systems and processes to automate and truly harness the power of AI.
Artificial intelligence belongs to the development of computer systems able to act as the human mind, such as visual perception, speech recognition, decision making, and translation between the languages. The predictions are made that the global population will reach about10 billion people in 2050, enhanced agriculture production to meet the food demands in need of the hour which is about the 70% increase in food production. Farm enterprise needs new and advanced technologies to overcome these challenges. By using artificial intelligence we can overcome these demands. Just imagine what will happen if the farm is under the control of such machinery which acts like humans and store information like human accurately and efficiently.
Washington D.C. [USA], July 14 (ANI): Researchers developed a new artificial intelligence (AI) tool for detecting unfair discrimination such as race or gender. Preventing unfair treatment of individuals on the basis of race, gender or ethnicity, for example, been a long-standing concern of civilized societies. However, detecting such discrimination resulting from decisions, whether by human decision-makers or automated AI systems, can be extremely challenging. This challenge is further exacerbated by the wide adoption of AI systems to automate decisions in many domains including policing, consumer finance, higher education, and business. "Artificial intelligence systems such as those involved in selecting candidates for a job or for admission to a university are trained on large amounts of data. But if these data are biased, they can affect the recommendations of AI systems," said Vasant Honavar, one of the researchers of the study presented at the meeting of The Web Conference.