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) …
In analytics, we retrieve information from various data sources; it can be structured or unstructured. The biggest challenge here is to retrieve information from unstructured data mainly texts. Here machine learning comes into the picture to overcome this challenge. Different algorithms have been designed in different platforms but here we will discuss one technique that can be applied in python. The process can be explained better by an example.
Real-world examples make the abstract description of machine learning become concrete. In this post you will go on a tour of real world machine learning problems. You will see how machine learning can actually be used in fields like education, science, technology and medicine. Each machine learning problem listed also includes a link to the publicly available dataset. This means that if a particular concrete machine learning problem interest you, you can download the dataset and start practicing immediately.
It's flu season, and many of us find ourselves glancing nervously at anyone coughing or sniffling in our vicinity. But how, besides shielding ourselves from public sneezers, do we avoid coming into contact with infections? It turns out, our brains are quite finely tuned to detecting illness in others. New research suggests that subtle facial cues alert us to infections mere hours after they take hold. This research could one day help train AI systems to detect illness as well.
Yet this common disease of the bones is one of the most difficult to detect and prevent in its early stages. In the U.S. alone, osteoarthritis is responsible for the majority of total knee and hip replacements. The most prevalent form of the disease affects the knees, occurring in 10 percent of men and 13 percent of women over the age of 60. These numbers are only expected to grow due to aging populations and the obesity epidemic -- with huge costs on public health systems and well-being. Hoping to turn this trend around, ImageBiopsy Lab, an Austrian startup and member of our Inception program, is using deep learning to diagnose osteoarthritis of the knees much more efficiently and cost-effectively.
Artificial intelligence is no more going to remain the secret sauce of some of the biggest technology companies. Google on Wednesday unveiled'Cloud AutoML', which would help businesses with limited machine learning expertise start building their own high-quality custom models using advanced techniques provided by the Internet giant. The applications range from automating product attributes like patterns and necklines styles for clothing companies to helping various organisations conserve the world's wildlife by analysing and tagging millions of images of various animal species. "There are bigger, greater opportunities waiting to be unlocked by AI," said Fei-Fei Li, chief scientist of AI and machine learning at Google Cloud, during a webcast with reporters. Google said the new platform would help less-skilled engineers build powerful AI systems they previously only "dreamed of."
The Boston public access station WGBH has partnered with PBS for another short series in its long-running Nova family of programs. Nova Wonder will follow three researchers exploring big scientific mysteries. Each episode tackles a different complex question: Do animals have a secret language? Which AI technologies could surpass human abilities? How ethical is it to grow life in a lab?
Ensuring endpoint security has always been a key challenge for enterprises. But whereas it was once enough to install antivirus (AV) software across a network and expect a reasonable level of endpoint protection, this is no longer the case. With the proliferation of bring your own device policies in the workplace and the wide variety of smart devices available to end users, not to mention the growth of IoT, there are more endpoints than ever, and endpoint security has never been more under threat. Get the latest from CSO by signing up for our newsletters. Various studies put the number of security breaches originating at endpoints between 70 and 95 per cent.
The new guidance around artificial intelligence in federal IT seems to boil down to this: Get beyond the hype. IT leaders, lawmakers and federal technology partners seem to be getting the message and are seeking practical and realistic ways to incorporate AI into how the government runs. IT leaders are starting to use AI in more applications, and agencies are thinking about how AI can make their operations more efficient and enhance national security. Meanwhile, the growth of AI in the consumer market and in government is pushing lawmakers to consider how AI will impact society and how it might be regulated. "Agency use of AI is accelerating in a number of areas based on machine learning technology, including cyberwarfare, robotics, border security, healthcare and virtual assistants," Deniece Peterson, Deltek's director of federal market analysis, told FedScoop last month.
At Bossa Nova we create service robots for the global retail industry. Our robots' mission is to make stores run efficiently by automating the collection and analysis of on-shelves inventory data in large scale stores. Navigating smoothly along the aisles, we circulate autonomously among busy customers and employees. If we were a self- driving car we'd be operating at level 5 autonomy. Yep, it is possible to move, scan and analyze all at the same time.
AI is the compelling topic of tech conversations du jour, yet within these conversations confusion often reigns – confusion caused by loose use of AI terminology. The problem is that AI comes in a variety of forms, each one with its own distinct range of capabilities and techniques, and at its own stage of development. Some forms of AI that we frequently hear about, such as Artificial General Intelligence, the kind of AI that might someday automate all work and that we might lose control of – may never come to pass. Others are doing useful work and are driving growth in the high performance sector of the technology industry. These definitions aren't meant to be the final word on AI terminology, the industry is growing and changing so fast that terms will change and new ones will be added.