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) …
Last June, Facebook described how it uses AI to help find and take down terrorist content on its platform and in November, the company said that its AI tools had allowed it to remove nearly all ISIS- and Al Qaeda-related content before it was flagged by a user. Its efforts to remove terrorist content with artificial intelligence came up frequently during Mark Zuckerberg's Congressional hearings earlier this month and the company's lead policy manager of counterterrorism spoke about the work during SXSW in March. Today, Facebook gave an update of that work in an installment of its Hard Questions series. Facebook defines terrorism as, "Any non-governmental organization that engages in premeditated acts of violence against persons or property to intimidate a civilian population, government or international organization in order to achieve a political, religious or ideological aim." And it notes that governments aren't included due to a "general academic and legal consensus that nation-states may legitimately use violence under certain circumstances."
In this post, I shall explain object detection and various algorithms like Faster R-CNN, YOLO, SSD. We shall start from beginners' level and go till the state-of-the-art in object detection, understanding the intuition, approach and salient features of each method. Image classification takes an image and predicts the object in an image. What would our model predict? To solve this problem we can train a multi-label classifier which will predict both the classes(dog as well as cat).
Cyber fraud costs organizations billions of dollars each year, and its financial impact continues to climb as criminals are getting smarter and their attacks more complex. While the increasing need for rapid and complex fraud risk detection is common in many sectors, it is perhaps most acute among financial institutions and online merchants. Competition is fierce in these highly digitized markets, and margins are razor-thin. Customers are extremely demanding, and constantly seek better, more user-friendly payment options and channels. Cross-channel fraud detection has been an area of focus for both business and security leaders for nearly a decade.
The US Army is developing a machine learning method for identifying faces from thermal imagery. Soon the American government will be able to film people from outside of buildings, using cameras that can see through walls in near-total darkness, and an AI will recognize the people in the images. Army Research Laboratory (ARL) scientists Benjamin S. Riggan, Nathaniel J. Short, and Shuowen Hu recently released a white paper detailing military efforts to develop a method for applying facial recognition technology to images taken using thermal imaging devices. When using thermal cameras to capture facial imagery, the main challenge is that the captured thermal image must be matched against a watch list or gallery that only contains conventional visible imagery from known persons of interest. Such devices are common, especially in military use.
Google is betting that the future of healthcare is going to be structured data and AI. The company is applying AI to disease detection, new data infrastructure, and potentially insurance. In this report we explore Google's many healthcare initiatives and areas of potential future expansion. Google has always seen itself as more than a search and advertising company. Now it's turning its focus to healthcare, betting that its AI prowess can create a powerful new paradigm for the detection, diagnosis, and treatment of disease. "So tomorrow, if AI can shape healthcare, it has to work through the regulations of healthcare … In fact, I see that as one of the biggest areas is where the benefits will play out for the next 10 – 20 years." In short, Google seems to be going after the healthcare space from every possible angle. For example, did you know that Google has a project to release sterilized mosquitoes to control the spread of infectious disease? Or that the company has started a limited commercial rollout of its diabetes management program? Or that it appears to be exploring insurance? Note: For simplicity we use "Google" as shorthand for the larger Alphabet company, under which many of these healthcare initiatives fall. We explain the Alphabet structure below. As Google enters healthcare, it's leaning heavily on its expertise in AI. Health data is getting digitized and structured, from a new electronic record standard to imaging to DNA sequencing.
We frequently hear about Machine Learning in the media, especially since the recent wave of interest in deep-learning. The perpetual improvement of Machine Learning techniques combined with the ever increasing amount of data that are stored suggests endless new applications. Many innovative solutions emerge: autonomous driving, next generation supermarkets with implicit payment, next generation chatbots that can interact with you as human beings would do, and so on. More than ever, the future seems within reach. But the more extravagant and original the application is, the more the layman is put off.
Suprema ID is taking the opportunity of next week's ID4Africa 2018 exhibition to launch new fingerprint scanner solutions featuring liveness detection based on machine learning technology. The RealScan-D is a dual finger enrollment scanner with FBI IAFIS Appendix F certification, while the RealScan-G1 is a FAP30 enrollment scanner boasting of IP54-rated water and dust resistance. Both devices are compact and portable, and both feature a wide platen facilitating the easy collection of detailed fingerprints. Most importantly, according to Suprema, both devices feature machine learning Live Finger Detection technology designed to identify synthetic materials used in spoofing such as clay, silicon, paper, film, and rubber. In a statement, Suprema CEO Bogun Park suggested that his firm's new technology meets a market trend, explaining, "We are experiencing increasing demand for anti-spoofing technology on our live scanning devices."
The financial services industry has witnessed considerable hype around artificial intelligence (AI) in recent months. AI indeed appears to be the new gold rush for large organisations and FinTech companies alike. However, with little common understanding of what AI really entails, there is growing fear of missing the boat on a technology hailed as the'holy grail of the data age.' Devising an AI strategy has therefore become a boardroom conundrum for many business leaders. How did it come to this – especially since less than two decades back, most popular references of artificial intelligence were in sci-fi movies?
On Tuesday, for example, 34 companies including Microsoft, Oracle and Facebook signed the Cybersecurity Tech Accord, publicly committing to protect internet users, work together and improve resilience in the space. But outside of large-scale initiatives, the basics, such as malware detection, have a long road ahead as the cyberattacks keep rolling in. Advancements in AI and ML on the enterprise side are important to counter hackers also utilizing the technology to automate attacks. The most effective kind of malware is a strain that hits without a business ever knowing, but advanced detection capabilities harnessing AI and ML are steadily helping cybersecurity teams overcome the odds. But without good data, good defensive and detection measures are hard to build.
Data science and machine learning are some of the top buzzwords in the technical world today. The resurging interest in machine learning is due to the same factors that have made data mining and Bayesian analysis more popular than ever. This video is your entry point to machine learning. It starts with an introduction to machine learning and the Python language and shows you how to complete the necessary setup. Moving ahead, you will learn all the important concepts such as exploratory data analysis, data preprocessing, feature extraction, data visualization and clustering, classification, regression, and model performance evaluation.