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) …
We've just begun to scratch the surface of the impact of machine learning on the enterprise. Organizations are applying machine learning algorithms to business processes to automate manual tasks and identify patterns in transactional data to drive strategic decisions. Many applications are focused on efficiency and automation, but that trend is shifting. More and more businesses are using machine learning to develop disruptive new business models. What does this mean for your organization?
Amazon SageMaker is a fully managed service for developers and data scientists to quickly build, train, deploy, and manage their own machine learning models. AWS also introduced AWS DeepLens, a deep learning-enabled wireless video camera that can run real-time computer vision models to give developers hands-on experience with machine learning. And, AWS announced four new application services that allow developers to build applications that emulate human-like cognition: Amazon Transcribe for converting speech to text; Amazon Translate for translating text between languages; Amazon Comprehend for understanding natural language; and, Amazon Rekognition Video, a new computer vision service for analyzing videos in batches and in real-time. Today, implementing machine learning is complex, involves a great deal of trial and error, and requires specialized skills. Developers and data scientists must first visualize, transform, and pre-process data to get it into a format that an algorithm can use to train a model.
Today on the VOICES stage, BoF and Google announced a partnership designed to explore and demonstrate the potential applications of artificial intelligence in fashion, and begin a dialogue between the industry and one of the global leaders in machine learning. In its first instance, the partnership has prompted a series of experiments with data sets from BoF's Fashion Week coverage, the early fruits of which were unveiled before VOICES attendees here in Oxfordshire. Representing Google was Amit Sood, who, in 2011, founded what became the Google Cultural Institute, a non-profit arm of the company, now housed in a grand hôtel particulier in the 9th arrondissement of Paris, that has partnered with over 1,300 museums and foundations to digitise everything from the Dead Sea Scrolls to Marc Chagall's ceiling at the Opéra Garnier, making them accessible on a platform called Google Arts & Culture. The institute is now experimenting with what machine learning can enable when applied to this catalogue, with a focus on fashion as well as art. Sood showed members of the VOICES community some of the work his team of engineers have been doing and, alongside BoF's Imran Amed, shared the results of applying Google's machine learning algorithms to over 70,000 runway looks from BoF's coverage of the last fours years of shows.
The Federal Drug Administration just cleared a new band for the Apple Watch that monitors the electrical rhythms in your heart. After a two-year process to satisfy the FDA's stringent requirements, AliveCor announced today that the Kardia Band is now available for purchase for $199. It's a mobile electrocardiogram (EKG), which measures a heart's electrical activity and has traditionally been used by doctors to identify abnormal cardiac rhythms. "It's a regulated measure of physiology by the FDA. Doctors can recognize over 100 conditions when they see an EKG," AliveCor CEO Vic Gundotra told Mashable.
Wall Street is big business, and it is about to become even bigger with the rise of big data. It is every investor's dream to have prior knowledge of the direction of the market before it happens, which is why financial investment firms are driven to mine for data rather than for gold in the information economy. Traditionally, investors have based their decisions on fundamentals, intuition, and analysis drawn from traditional data sources, such as quarterly earnings reports, financial statement filings to the U.S. Securities and Exchange Commission (SEC), historical market data, institutional research reports and sometimes the so-called "expert networks." The new data-driven paradigm, fueled by new alternative data sources, high performance computing and predictive analytics, offers a more robust framework to generate data-driven investment theses. Data – from satellite images of areas of interest, automated drones, people-counting sensors, container ships' positions, credit card transactional data, jobs and layoffs reports, cell phones, social media, news articles, tweets, online search queries – is now the most valuable commodity for Wall Street.
Drones controlled by humans may soon give in to ones flown completely using artificial intelligence, a new experiment by the NASA Jet Propulsion Laboratory (JPL) has demonstrated. In the demonstration, NASA researchers pitted a human-controlled drone against one controlled by AI. The findings were published on NASA's website and a video of the race was uploaded on its YouTube website Tuesday. "We pitted our algorithms against a human, who flies a lot more by feel. You can actually see that the A.I. flies the drone smoothly around the course, whereas human pilots tend to accelerate aggressively, so their path is jerkier," Rob Reid, the project's task manager, said in a press release.
BROOKLYN, N.Y.--(BUSINESS WIRE)--Pienso, the leading machine learning platform for non-programmers, today announces the close of a $2.1 million seed round. Led by Eniac Ventures, with participation from SoftTech VC, Indicator Ventures, and E14 Fund, Pienso is focused on democratizing machine learning for domain experts who are non-programmers with no technical or data scientist experience. The funding allows the company to scale operations. "Investment by large enterprises in machine learning is rapidly accelerating as corporations spin up massive data lakes to garner insights into their business. However, it is costly, challenging to integrate and before now required data scientists on staff," said Vic Singh, general partner at Eniac.
The clinical assessment of suicidal tendency is currently done only by a psychological assessment which essentially involves asking the question -- Are you suicidal? According to a clinical research published by United States National Library of Medicine in a study called "Clinical Correlates of inpatient suicide," four in five patients who died by suicide had denied such tendencies in psychological assessment. According to a new study titled "Machine learning of neural representations of suicide and emotion concepts identifies suicidal youth" by researchers from the University of Pittsburgh and Carnegie Mellon University, artificial intelligence models may succeed where psychologists don't in detect such tendencies. The researchers put 17 adults with suicidal ideation using 17 control subjects who were not known to have such tendencies and put them in fMRI scanner to measure which areas of the brain would be activated when the subjects would be thinking about those keywords. For example, the study states that words like death and cruelty activated left the superior medial frontal area of the brain and the medial frontal/anterior cingulate respectively.
Instead of preprogramming software to complete a specific task, as narrow AI does, machine learning uses algorithms that allow a computer to learn from the vast amounts of data it receives so it can complete a task on its own. International Business Machines uses deep learning powered by NVIDIA's graphics processing units (GPUs) to comb through medical images to find cancer cells. The company makes the graphics processors that are integral in AI, machine learning, and deep learning, and lots of companies already look to NVIDIA's hardware to make their AI software a reality. The Motley Fool owns shares of and recommends Alphabet (A shares), Alphabet (C shares), Amazon, Facebook, and Nvidia.
Smartwatches range from simple fitness tracking wristbands to devices like the Apple Watch, which has a surprising range of functionality comparable even to smartphones. Hristijan Gjoreski of the University of Sussex said in a press release, "Current activity-recognition systems usually fail because they are limited to recognizing a predefined set of activities, whereas of course human activities are not limited and change with time." He continued in stating that, "Here we present a new machine-learning approach that detects new human activities as they happen in real time, and which outperforms competing approaches." By eliminating the limits of defined activity as older models do, smartwatches would be able to better track and record human activity.