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
Monthly Archives: June 2019
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
AI Ethics at War – When AI Governance Shifts from Cooperation to Competition
Today, the world of AI ethics is a harmonious ecosystem of organizations with uncontroversial and reasonable, respectable aims.
They share conferences, include each other in thought leadership, and develop white papers together – in order to discover frameworks for governing AI and handling issues around privacy, security, bias, and individual rights.
This makes sense, for now – because AI ethics is a means to great power. Read More
Software Engineering for Machine Learning: A Case Study
Recent advances in machine learning have stimulated widespread interest within the Information Technology sector on integrating AI capabilities into software and services.This goal has forced organizations to evolve their development processes. We report on a study that we conducted on observing software teams at Microsoft as they develop AI-based applications. We consider a nine-stage workflow process informed by prior experiences developing AI applications (e.g., search and NLP) and data science tools (e.g. application diagnostics and bug reporting). We found that various Microsoft teams have united this workflow into preexisting, well-evolved, Agile-like software engineering processes, providing insights about several essential engineering challenges that organizations may face in creating large-scale AI solutions for the marketplace. We collected some best practices from Microsoft teams to address these challenges.In addition, we have identified three aspects of the AI domain that make it fundamentally different from prior software application domains: 1) discovering, managing, and versioning the data needed for machine learning applications is much more complex and difficult than other types of software engineering, 2) model customization and model reuse require very different skills than are typically found in software teams, and 3) AI components are more difficult to handle as distinct modules than traditional software components — models may be “entangled” in complex ways and experience non-monotonic error behavior. We believe that the lessons learned by Microsoft teams will be valuable too their organizations. Read More
AI adoption is being fueled by an improved tool ecosystem
In this post, I share slides and notes from a keynote that Roger Chen and I gave at the 2019 Artificial Intelligence conference in New York City. In this short summary, I highlight results from a — survey (AI Adoption in the Enterprise) and describe recent trends in AI. Over the past decade, AI and machine learning (ML) have become extremely active research areas: the web site arxiv.org had an average daily upload of around 100 machine learning papers in 2018. With all the research that has been conducted over the past few years, it’s fair to say that we now have entered the implementation phase for many AI technologies. Companies are beginning to translate research results and developments into products and services. Read More
Federated Learning — Google
Practical Secure Aggregation for Privacy-Preserving Machine Learning
We design a novel, communication-efficient, failure-robust protocol for secure aggregation of high-dimensional data. Our protocol allows a server to compute the sum of large, user-held data vectors from mobile devices in a secure manner (i.e. without learning each user’s individual contribution), and can be used, for example,in a federated learning setting, to aggregate user-provided model updates for a deep neural network. We prove the security of our protocol in the honest-but-curious and active adversary settings,and show that security is maintained even if an arbitrarily chosen subset of users drop out at any time. We evaluate the efficiency of our protocol and show, by complexity analysis and a concrete implementation, that its runtime and communication overhead remain low even on large data sets and client pools. For 16-bit input values, our protocol offers 1.73×communication expansion for210users and220-dimensional vectors, and 1.98×expansion for214users and224-dimensional vectors over sending data in the clear. Read More
Rick's Cafe AI 