Daily Archives: June 17, 2019
Webinar Wrap-up: How to Build a Career in AI and Machine Learning
Artificial Intelligence (AI) made headlines recently when people started reporting that Alexa was laughing unexpectedly. Those news reports led to the usual jokes about computers taking over the world, but there’s nothing funny about considering AI as a career field. Just the fact that five out of six Americans use AI services in one form or another every day proves that this is a viable career option. Read More
The Key To Unlocking The Power Of AI: Data Trading
One of the major hurdles companies face in transforming to a Digital Supply Chain is their inability to get data from customers and suppliers—or even from other departments in their own company. Nothing new, right?
What is new is the idea of “trading data” to overcome that hurdle and use as a catalyst for Digital Supply Chain transformation. Let me explain.
Companies are aggressively turning to artificial intelligence and machine learning (AI/ML) to gain a competitive advantage. But for that strategy to succeed, companies must develop algorithms that rely on AI/ML technology to run their business. And what is the life force behind algorithms? Data. Lots of data. That makes data trading, internally and with customers and suppliers, essential to unlocking the power of AI/ML.
The critical management question is how to do it? Read More
Meet The World’s Most Valuable AI Startup: China’s SenseTime
In just four years, SenseTime went from being an academic project to become the world’s most valuable artificial intelligence (AI) company with a current valuation of $4.5 billion. Based in China, the company has a portfolio of 700 clients and partners, including the Massachusetts Institute of Technology (MIT), Qualcomm, Honda, Alibaba, Weibo, and more. They use their proprietary artificial intelligence and machine vision technology to drive its success and “redefine human life as we know it.” With the number of core technologies, products, and services SenseTime offers, it’s hard to believe it’s such a young company. Here are just a few ways SenseTime uses artificial intelligence to “power the future.” Read More
Is The Future Of Artificial Intelligence Tied To The Future Of Blockchain?
Since the beginning of modern times, each industrial revolution was driven by different automation. While factory machines and fossil fuels drove the previous industrial revolutions, the on-going automation revolution is based on data-driven artificial intelligence (AI). Understanding its impact and what will be required to support the AI-driven automation revolution is a fundamental necessity.
So, as we evaluate the impact and the support needed to harness this automation revolution, it seems that at the center of this revolution is the growing need for computing power. There are indicators that raw computing power is on its way to replacing fossil fuels and will be the most valued fuel in the rapidly emerging intelligence age. From where we are to where we want to reach in our intelligence automation journey, further advances in artificial intelligence require enormous amounts of computational power. Read More
Microsoft Offers 'Premade' No-Code Artificial Intelligence
Big software vendors like to feather out their nest with a bed of ancillary services and functions designed to position themselves as one-stop-shop solution providers. Where successful, this means that customers can potentially avoid software integration and update issues that might otherwise hamper their day-to-day operations. It is also meant to provide customers with a no-brainer approach to staying on that vendor’s platform and roadmap, which (in theory at least) avoids other incompatibilities created when customers bring about in house customizations.
In reality, almost every medium-sized business (and bigger) will always operate with a mix of technology platforms, different databases and device form factors — but aiming for Nirvana isn’t a bad idea, even if most of us never get there. Read More
Habana Labs launches its Gaudi AI training processor
Habana Labs, a Tel Aviv-based AI processor startup, today announced its Gaudi AI training processor, which promises to easily beat GPU-based systems by a factor of four. While the individual Gaudi chips beat GPUs in raw performance, it’s the company’s networking technology that gives it the extra boost to reach its full potential.
Gaudi will be available as a standard PCIe card that supports eight ports of 100GB Ethernet, as well as a mezzanine card that is compliant with the relatively new Open Compute Project accelerator module specs. This card supports either the same 10 100GB Ethernet ports or 20 ports of 50GB Ethernet. The company is also launching a system with eight of these mezzanine cards. Read More
US Army trains StarCraft II AI; teaching drones to dodge thrown objects; and fighting climate change with machine learning
Drones that dodge, evade, and avoid objects – they’re closer than you think:
…Drones are an omni-use platform, and they’re about to get really smart…
The University of Maryland and the University of Zurich have taught drones how to dodge rapidly moving objects, taking a further step towards building semi-autonomous, adaptive small-scale aircraft. The research shows that drones equipped with a few basic sensors and some clever AI software can learn to dodge (and chase) a variety of objects. “To our knowledge, this is the first deep learning based solution to the problem of dynamic obstacle avoidance using event cameras on a quadrotor”, they write. Read More
The New Wilderness
The need to regulate online privacy is a truth so universally acknowledged that even Facebook and Google have joined the chorus of voices crying for change.
Writing in the New York Times last month, Google CEO Sundar Pichai argued that it is “vital for companies to give people clear, individual choices around how their data is used.” Like all Times opinion pieces, his editorial included multiple Google tracking scripts served without the reader’s knowledge or consent. Had he wanted to, Mr. Pichai could have learned down to the second when a particular reader had read his assurance that Google “stayed focused on the products and features that make privacy a reality.”
Writing in a similar vein in the Washington Post this March, Facebook CEO Mark Zuckerberg called for Congress to pass privacy laws modeled on the European General Data Protection Regulation (GDPR). That editorial was served to readers with a similar bouquet of non-consensual tracking scripts that violated both the letter and spirit of the law Mr. Zuckerberg wants Congress to enact.
This odd situation recalls the cigarette ads in the 1930’s in which tobacco companies brought out rival doctors to argue over which brand was most soothing to the throat. Read More
Learning Sparse Networks Using Targeted Dropout
Neural networks are easier to optimise when they have many more weights than are required for modelling the mapping from inputs to outputs. This suggests a two-stage learning procedure that first learns a large net and then prunes away connections or hidden units. But standard training does not necessarily encourage nets to be amenable to pruning. We introduce targeted dropout, a method for training a neural network so that it is robust to subsequent pruning. Before computing the gradients for each weight update, targeted dropout stochastically selects a set of units or weights to be dropped using a simple self-reinforcing sparsity criterion and then computes the gradients for the remaining weights. The resulting network is robust to post hoc pruning of weights or units that frequently occur in the dropped sets. The method improves upon more complicated sparsifying regularisers while being simple to implement and easy to tune. Read More
Rick's Cafe AI 