Machine Learning

Home Office to fund use of AI to help catch dark web paedophiles

The Guardian

Artificial intelligence could be used to help catch paedophiles operating on the dark web, the Home Office has announced. The government has pledged to spend more money on the child abuse image database, which since 2014 has allowed police and other law enforcement agencies to search seized computers and other devices for indecent images of children quickly, against a record of 14m images, to help identify victims. The investment will be used to trial aspects of AI including voice analysis and age estimation to see whether they would help track down child abusers. Earlier this month, the chancellor, Sajid Javid, announced £30m would be set aside to tackle online child sexual exploitation, with the Home Office releasing more information on how this would be spent on Tuesday. There has been debate over the use of machine learning algorithms, part of the broad field of AI, with the government's Centre for Data Ethics and Innovation developing a code of practice for the trialling of the predictive analytical technology in policing.

This Site Shows You What AI Really Thinks of You


Have you ever wondered what a computer thinks of you when it automatically detects your face before applying a cat filter? Thanks to a new AI tool, you can find out, but fair warning: the reality isn't pretty. "ImageNet Roulette" is a website created by programmer Leif Ryge for researcher Kate Crawford and artist Trevor Paglen's recent art exhibit "Training Humans." The site takes your photo and runs it through some common machine learning software before returning the labels that the AI decided to apply to you. As numerous people discovered (and tweeted about) while using the tool, these labels are often weird, mean, racist, and misogynistic.

Arkansas Scientists Employ Machine Learning to Manage Corn Crops More Efficiently


Professors Jia Di, left, and Trent Roberts inspect a prototype corn sensor set up in a test plot at the Arkansas Agricultural Research and Extension Center. FAYETTEVILLE, Ark. – A team of researchers from the University of Arkansas System Division of Agriculture and the University of Arkansas College of Engineering is designing tiny sensors that can be placed in corn stalks to monitor water, nitrogen and potassium needs in real time. The data collected from those sensors -- matched with geographic, weather and other environmental data -- will feed machine learning software to develop models that will be able to predict when a crop will need those inputs before the conditions exist. Those predictive models can help corn growers give their crops exactly the water and nutrients they need, before they experience stress, to achieve the best possible yields without wasting resources. The collaborative research by the division's Arkansas Agricultural Experiment Station and the university's College of Engineering is supported by the Chancellor's Discovery, Creativity, Innovation and Collaboration Fund.

Introduction to machine learning in JavaScript using TensorFlow - O'Reilly TensorFlow World in Santa Clara 2019


Sandeep Gupta is a product manager at Google, where he helps develop and drive the road map for TensorFlow--Google's open source library and framework for machine learning--for supporting machine learning applications and research. His focus is on improving TensorFlow's usability and driving adoption in the community and enterprise. Sandeep is excited about how machine learning and AI are transforming lives in a variety of ways, and he works with the Google team and external partners to help create powerful, scalable solutions for all. Previously, Sandeep was the technology leader for advanced imaging and analytics research and development at GE Global Research with specific emphasis on medical imaging and healthcare analytics.

The 10 most important moments in AI (so far)


This article is part of Fast Company's editorial series The New Rules of AI. More than 60 years into the era of artificial intelligence, the world's largest technology companies are just beginning to crack open what's possible with AI--and grapple with how it might change our future. Click here to read all the stories in the series. Artificial intelligence is still in its youth. But some very big things have already happened.

AI Can Now Pass School Tests but Still Falls Short on the Turing Test


From winning at Go to passing eighth grade level multiple choice tests, AI is making rapid advances. But its creativity still leaves much to be desired. On September 4, 2019, Peter Clark, along with several other researchers, published "From'F' to'A' on the N.Y. Regents Science Exams: An Overview of the Aristo Project " The Aristo project named in the title is hailed for the rapid improvement it has demonstrated when it tested the way eighth-grade human students in New York State are tested for their knowledge of science. The researchers concluded that this is an important milestone for AI: "Although Aristo only answers multiple choice questions without diagrams, and operates only in the domain of science, it nevertheless represents an important milestone towards systems that can read and understand. The momentum on this task has been remarkable, with accuracy moving from roughly 60% to over 90% in just three years."

Ensemble methods: bagging, boosting and stacking


This post was co-written with Baptiste Rocca. This old saying expresses pretty well the underlying idea that rules the very powerful "ensemble methods" in machine learning. Roughly, ensemble learning methods, that often trust the top rankings of many machine learning competitions (including Kaggle's competitions), are based on the hypothesis that combining multiple models together can often produce a much more powerful model. The purpose of this post is to introduce various notions of ensemble learning. We will give the reader some necessary keys to well understand and use related methods and be able to design adapted solutions when needed.

"Father of Machine Learning", the Chief AI Scientist of Squirrel AI Learning, Tom Mitchell Delivered an Opening Speech at the 2019 World Artificial Intelligence Conference(WAIC): AI for a Brighter World!


SHANGHAI, China, Sept. 16, 2019 (GLOBE NEWSWIRE) -- On August 29th, with the theme of "Intelligent Connectivity, Infinite Possibilities", the 2019 World Artificial Intelligence Conference (WAIC), co-sponsored by the National Development and Reform Commission, the Ministry of Science and Technology, the Ministry of Industry and Information Technology, National Internet Information Office, Chinese Academy of Sciences, Chinese Academy of Engineering and Shanghai Municipal People's Government, was solemnly held in Shanghai. More than 500 top universities, international organizations and the world's most influential scientists, entrepreneurs and investors in the field of artificial intelligence gathered in Shanghai. Turing Award winners Raj Reddy and Manuel Blum, former Dean of the School of Computer Science at CMU & Chief AI Scientist of Squirrel AI Learning Tom Mitchell, Nobel Prize winner George Smoot, "Father of Machine Learning", Finn E. Kydland, Swiss AI Lab IDSIA Scientific Director Jürgen Schmidhuber Co-founder and CEO of Tesla Elon Musk, Chairman of the Board of Directors and CEO of Tencent Pony (Huateng) Ma, Co-chairman of the United Nations High-level Group on Digital Cooperation Jack Ma etc., delivered brilliant speeches and conversations respectively. In the top-leader conversation session, Elon Musk, Co-founder and CEO of Tesla, conducted an in-depth conversation with Jack Ma, Co-chairman of the United Nations High-level Group on Digital Cooperation. When it comes to education, Musk said, "The lecture is the worst because it's too slow. It's hard to make fewer mistakes for us in predicting the future, but you have to try first, and then to adjust it according to the errors you have predicted before."

7 Effective Ways to Deal With a Small Dataset


Big data and data science are concepts often heard together. It is believed that nowadays there are large amounts of data and that data science can draw valuable insights from all these terabytes of information. However, in a practical scenario, you will often have limited data to solve a problem. Gathering a big dataset can be prohibitively expensive or simply impossible (e.g., only having records from a certain time period when doing time series analysis). As a result, there is often no choice but to work with a small dataset, trying to get as accurate predictions as possible.

HPE containerizes machine learning model development - SiliconANGLE


Hewlett Packard Enterprise Co. today is expanding its reach into artificial intelligence development with a software platform that supports the full lifecycle of machine learning model construction and deployment using the self-contained software environments called containers. HPE ML Ops provides for the rapid rollout of machine learning workloads across on-premises, public cloud and hybrid cloud environments. The idea is to enable development teams to employ processes similar to those used in DevOps, the rapid application-building technique that that involves frequent code releases and constant refinement. The result is reductions in model deployment times from months to days, HPE said. The company is attacking a common problem with machine learning projects, which is a lack of resources and operational processes to deploy them.