- Read the full ABC article and watch the video interview to learn more about Tanmay and his work in the field of AI.
- The Australian Broadcasting Corporation (ABC) recently profiled 13-year-old Canadian tech prodigy Tanmay Bakshi who started using computers at age five, launched his first app at age nine, and has been working with IBM’s AI and cognitive APIs for a couple of years now.
- ABC notes: “the Canadian teen has become a global force in programming and commands more than 20,000 subscribers on his YouTube channel that teaches computer coding.”
- He is currently in Australia for the IBM Watson Summit, which brings together experts in artificial intelligence to discuss how the technology can help people and businesses in the future.
- You can also watch Tanmay’s video, “IBM Watson, Machine Learning: How to use the “Retrieve and Rank” service in IBM Bluemix”, one of 80 tutorials he has created and made available on the “Tanmay Teaches” YouTube channel.
A profile of 13-year-old Canadian tech prodigy Tanmay Bakshi who started using computers at age five and works with IBM’s AI and cognitive APIs.
Continue reading “Meet the 13-year-old prodigy taking IBM and artificial intelligence by storm”
- A key learning, is that the way in which these SVM’s are structured can actually have a significant impact on how much training data has to be applied, for example, a naive approach would have been as follows:
This approach requires that for every additional sub-category, two new SVM’s be trained – for example, the addition of a new class for ‘Swimwear’ would require an additional SVM under Men’s and Women’s – not to mention the potential complexity of adding a ‘Unisex’ class at the top level.
- We were able to avoid a great deal of labelling& training work, by flattening our data structures into many sub-trees like so:
By decoupling our classification structure from the final hierarchy, it is possible to generate the final classification by traversing the SVM hierarchy with each document, and interrogating the results with simple set-based logic such as:
Mens Slim-fit jeans = (Mens and Jeans and Slim Fit) and not Womens
This approach vastly reduces the number of SVM’s required to classify documents, as the resultant sets can be intersected to represent the final classification.
- For example – adding a top-level ‘Children’s’ class – would immediately allow the creation of an entire dimension of new Children’s categories (children’s jeans, shirts, underwear, etc), with minimal additional training data (Only one additional SVM):
Because of the structure we chose, one key insight that we were able to leverage, was that of re-using training data, via linked data relationships.
- For example, given some basic domain knowledge of the categories – we know for certain that ‘Washing machines’ can never be ‘Carpet cleaners’
By adding the ability to link ‘Exclude data’, we can heavily bolster the amount ‘Negative’ training examples for the ‘Washing machines’ SVM by adding to it the ‘Positive’ training data from ‘Carpet cleaners’ SVM.
- This approach has a nice uptick, in that whenever the need arises to add some additional training data to improve the ‘Carpet Cleaners’ SVM – it inadvertently improves the ‘Washing machines’ class, via linked negative data.
In many cases, getting enough well-labelled training data is a huge hurdle for developing accurate prediction systems. Here is an innovative approach which uses SVM to get the most from training data.
Continue reading “How to squeeze the most from your training data”
- Tesla CEO Elon Musk and Facebook CEO Mark Zuckerberg are waging a public debate over the merits of AI.
- Musk has said in the past that AI could be potentially very damaging to humans, and Zuckerberg recently called such doomsday predictions “irresponsible.”
- Musk responded on Twitter, calling Zuckerberg’s understanding of AI “limited.”
Elon Musk and Mark Zuckerberg are currently embroiled in a public debate over the future of AI.
Continue reading “Elon Musk and Mark Zuckerberg disagree about the future of AI”
- ‘The financial sector understands the value of innovation and there is real ambition to implement artificial intelligence around the world, especially in Europe and the US’
Synechron Inc., the global financial services consulting and technology services provider, has announced that nearly 60 financial institutions are set to implement artificial intelligence (AI) technology – with interest spanning across four continents.
- Currently, 57 financial institutions based in Europe, the US, Middle East and Asia are being helped to adopt AI technology by Synechron: 28% of these firms are based in Europe with UK headquartered institutions accounting for nearly half (45%) of the interest in Europe, and 23% of interest worldwide.
- >See also: 5 ways AI will impact the global business market in 2017
A further 26% involve natural language processing or natural language generation.
- Most (54%) of the interest from the UK is centered on robotic process automation, while 43% of US firms and 30% of firms based on the European continent are interested in adopting natural language processing or natural language generation technology.
- >See also: Digital investment doubles in the UK, led by IoT and AI
Faisal Husain, Synechron co-founder and CEO, said: “The financial sector understands the value of innovation and there is real ambition to implement artificial intelligence around the world, especially in Europe and the US.
Demand for artificial intelligence reaches four continents in three months, new figures from Synechron show
Continue reading “Demand for artificial intelligence goes global”
- AI Financing Is Seven Times Higher in US Than China
(Yicai Global) July 28 — Total financing in America’s artificial intelligence sector totaled USD20.7 billion from 2001 through 2016, making up 71.8 percent of AI financing worldwide, a new report from Chinese digital industry think tank Wuzhen Institute said.
- Despite possessing great potential for AI development, China’s overall strength in the field significantly lags behind the US in terms of both the number of companies and the scale of financing.
- AI in China has developed most rapidly during the last three years, with new firms formed during that time making up 55.38 percent of the total number of AI companies in China from 2000 through 2016, the report finds.
- Financing of China’s AI companies over the last three years also accounts for 93.59 percent of total domestic financing since 2000.
- Regionally, Beijing ranks first in China in terms of new AI companies and financing, representing 50 percent of the country’s total financing.
Total financing in America’s artificial intelligence sector totaled USD20.7 billion from 2001 through 2016, making up 71.8 percent of AI financing worldwide, a new report from Chinese digital industry think tank Wuzhen Institute said.
Continue reading “AI Financing Is Seven Times Higher in US Than China”
- Today’s most advanced weapons are already capable of “making decisions” using built-in smart sensors and tools.
- However, while these weapons rely on some sort of artificial intelligence (AI) technology, they typically don’t have the ability to choose their own targets.
- Creating such weapons is now Russia’s goal, according to the country’s defense officials and weapons developers.
- Russia certainly isn’t the first nation to explore militarized AI.
- The US plans to incorporate AI into long-range anti-ship missile, and China is supposedly working on its own AI-powered weapons.
“As of today, certain successes are available, but we’ll still have to work for several years to achieve specific results.”
Continue reading “Russia is building an AI-powered missile that can think for itself”
- Chef Watson can’t chop, dice, or julienne.
- But ask the chef for a recommendation on cooking with green olives, and his knowledge is vast, incorporating data points from a library of recipes and an encyclopedia of flavor profiles.
- One of the early applications of IBM’s Watson technology, Chef Watson’s intelligence is in food.
- The goal for Chef Watson, IBM says, is to “surprise and delight human chefs.”
- HBR enlisted two cooks to partner with Watson in the kitchen: Ming Tsai, a renowned professional chef, and Gretchen Gavett, an HBR editor and kitchen novice.
We asked IBM’s AI to create recipes and then had celebrity chef Ming Tsai cook them. Watch what happened.
Continue reading “Artificial Intelligence, Real Food”