A pioneer in artificial intelligence says conventional companies can still distinguish themselves in A.I. despite worries that tech giants like Google and Amazon have already won.
Andrew Ng, a prominent Silicon Valley executive and investor who previously led some of the biggest A.I. projects at Google and its Chinese rival Baidu, says the next wave of A.I. will be in industries in which the tech giants aren’t firmly rooted. Think manufacturing, agriculture, and healthcare. Read More
Monthly Archives: September 2019
Neural Network Attributions: A Causal Perspective
We propose a new attribution method for neural networks developed using first principles of causality (to the best of our knowledge, the first such). The neural network architecture is viewed as a Structural Causal Model, and a methodology to compute the causal effect of each feature on the output is presented. With reasonable assumptions on the causal structure of the input data,we propose algorithms to efficiently compute the causal effects, as well as scale the approach to data with large dimensionality. We also show how this method can be used for recurrent neural networks.We report experimental results on both simulated and real datasets showcasing the promise and usefulness of the proposed algorithm. Read More
World Economic Forum – 8 predictions on AI & Robotics
The Rise of T-1000: Artificial Intelligence on the Battlefield
Artificial Intelligence (AI) or machine learning is being used by military intelligence and at the high strategic level. The question is whether this technology will ever filter down to the soldier actually doing the fighting on the ground? Science fiction novels and movies suggest a system that can communicate with a warfighter in real time and provide situational awareness, but how far is the fiction from reality?
“The most important weapon is situation awareness, and there are AI-based tools to help a lot with this,” explained Jim Purtilo, associate professor of computer science at the University of Maryland. Read More
These Machine Learning Techniques Make Google Lens A Success
Google Lens was introduced a couple of years ago by Google in a move to spearhead the ‘AI first’ products movement. Now, with the enhancement of machine learning techniques, especially in the domain of image processing and NLP, Google Lens has scaled to new heights. Here we take a look at a few algorithmic based solutions that power up Google Lens:
Lens uses computer vision, machine learning and Google’s Knowledge Graph to let people turn the things they see in the real world into a visual search box, enabling them to identify objects like plants and animals, or to copy and paste text from the real world into their phone. Read More
Google is open-sourcing a tool for data scientists to help protect private information
Google today announced that it is open-sourcing its so-called differential privacy library, an internal tool the company uses to securely draw insights from datasets that contain the private and sensitive personal information of its users.
Differential privacy is a cryptographic approach to data science, particularly with regard to analysis, that allows someone relying on software-aided analysis to draw insights from massive datasets while protecting user privacy. It does so by mixing novel user data with artificial “white noise,” as explained by Wired’s Andy Greenberg. That way, the results of any analysis cannot be used to unmask individuals or allow a malicious third party to trace any one data point back to an identifiable source. Read More
Artificial intelligence and war
The contest between China and America, the world’s two superpowers, has many dimensions, from skirmishes over steel quotas to squabbles over student visas. One of the most alarming and least understood is the race towards artificial-intelligence-enabled warfare. Both countries are investing large sums in militarised artificial intelligence (AI), from autonomous robots to software that gives generals rapid tactical advice in the heat of battle. China frets that America has an edge thanks to the breakthroughs of Western companies, such as their successes in sophisticated strategy games. America fears that China’s autocrats have free access to copious data and can enlist local tech firms on national service. Neither side wants to fall behind. As Jack Shanahan, a general who is the Pentagon’s point man for AI, put it last month, “What I don’t want to see is a future where our potential adversaries have a fully AI-enabled force and we do not.”
AI-enabled weapons may offer superhuman speed and precision (see article). But they also have the potential to upset the balance of power. Read More
Google Adds ‘Structured Signals’ to Model Training
An effort to bring structure and meaning to huge volumes of varied data is being used to improve training of neural networks.
The technique, dubbed Neural Structured Learning (NSL) attempts to leverage what developers call “structured signals.” In model training, those signals represent the connections or similarities among labeled and unlabeled data samples. The ability to capture those signals during neural network training is said to boost model accuracy, especially when labeled data is lacking.
NSL developers at Google (NASDAQ: GOOGL) reported this week their framework can be used to build more accurate models for machine vision, language translation and predictive analytics. Read More
If you want to see the benefits of AI, forget moonshots and think boring
CTOs are trying to figure out what the benefits of AI could be for their enterprise. Spoiler alert: they’re pretty dull, and that’s okay, according to, academic and author, Tom Davenport
You hear a lot about wildly ambitious AI initiatives these days — from curing diseases and solving world hunger to reversing climate change. While ambition is great and all, the problem with AI moonshots is that they generally crash and burn. Who can forget when the MD Anderson Cancer Center blew $62 million on a project to use IBM Watson to treat cancer that was later shelved. It’s because of this harsh reality that Tom Davenport, distinguished professor of information technology and management at Babson College, believes that if enterprises ever want to see the benefits of AI, they must embrace the mundane.
While many CTOs might want to aim for the moon with their AI projects, speaking with Information Age at IPsoft’s Digital Workforce Summit 2019, Davenport argued the best results come to those who opt to tackle a series of smaller projects first. CTOs are trying to figure out what the benefits of AI could be for their enterprise. Spoiler alert: they’re pretty dull, and that’s okay, according to, academic and author, Tom Davenport. Read More
Successful AI Implementation Starts With People
Artificial intelligence is here, bringing with it tremendous promise for innovation and productivity. From automating simple processes to making more complex processes smarter, AI has the potential to drastically improve the way companies work. But before you rush to implement it, beware of a major issue that could derail your goals: overlooking your people.
Any successful AI venture depends on getting your team behind it, yet too many business leaders are focusing on the technology without considering the needs of their workforce. The goal of intelligent automation is not just to grow revenue and improve efficiency, but also to help people raise their potential through new opportunities and ways of working. That will only happen if you’ve thoughtfully planned for how AI will affect your people. If they’re underprepared or fearful of how intelligent automation could affect their jobs, or if you haven’t determined how AI will affect your people strategy, this change could be more chaotic than constructive. Read More
Rick's Cafe AI 