Collaborating Authors


'Minecraft Dungeons' aims to be more than 'baby's first Diablo'


The simple pitch for Minecraft Dungeons goes something like this: Two great games play great together. If you're a fan of Minecraft but haven't heard about Dungeons, an explanation is in order. The camera hangs overhead, giving you a wide view of the terrain as you smash and plunder your way to ever-more-powerful heights. The reality falls pretty close to that, too. If you've ever played Diablo, its sequels, or any of the games like them (such as Torchlight or Titan Quest), you'll feel right at home.

Multi-tier Automated Planning for Adaptive Behavior (Extended Version) Artificial Intelligence

A planning domain, as any model, is never complete and inevitably makes assumptions on the environment's dynamic. By allowing the specification of just one domain model, the knowledge engineer is only able to make one set of assumptions, and to specify a single objective-goal. Borrowing from work in Software Engineering, we propose a multi-tier framework for planning that allows the specification of different sets of assumptions, and of different corresponding objectives. The framework aims to support the synthesis of adaptive behavior so as to mitigate the intrinsic risk in any planning modeling task. After defining the multi-tier planning task and its solution concept, we show how to solve problem instances by a succinct compilation to a form of non-deterministic planning. In doing so, our technique justifies the applicability of planning with both fair and unfair actions, and the need for more efforts in developing planning systems supporting dual fairness assumptions.

TiFL: A Tier-based Federated Learning System Machine Learning

Federated Learning (FL) enables learning a shared model across many clients without violating the privacy requirements. One of the key attributes in FL is the heterogeneity that exists in both resource and data due to the differences in computation and communication capacity, as well as the quantity and content of data among different clients. We conduct a case study to show that heterogeneity in resource and data has a significant impact on training time and model accuracy in conventional FL systems. To this end, we propose TiFL, a Tier-based Federated Learning System, which divides clients into tiers based on their training performance and selects clients from the same tier in each training round to mitigate the straggler problem caused by heterogeneity in resource and data quantity. To further tame the heterogeneity caused by non-IID (Independent and Identical Distribution) data and resources, TiFL employs an adaptive tier selection approach to update the tiering on-the-fly based on the observed training performance and accuracy overtime. We prototype TiFL in a FL testbed following Google's FL architecture and evaluate it using popular benchmarks and the state-of-the-art FL benchmark LEAF. Experimental evaluation shows that TiFL outperforms the conventional FL in various heterogeneous conditions. With the proposed adaptive tier selection policy, we demonstrate that TiFL achieves much faster training performance while keeping the same (and in some cases - better) test accuracy across the board.

Enhance Your Search Applications with Artificial Intelligence


Users expect to see that friendly search box in their applications. They seem to really like it, because it's so simple to use. You don't need a user manual to figure out search. In fact, if your application doesn't have search, you'll be pelted with negative reviews. No wonder you see search in so many applications. It's very difficult to implement. We all know it's more than just simple text matching. Those of us with database backgrounds know that searching for "prefix*" is a lot easier than searching for "*suffix". And users want to do all sorts of weird searches like "*run*", which should match ran, or shrunken or brunt, or--you get the idea. Quick search results and performance are important, as is accuracy and ranking.

KNIME Desktop: the "killer app" for machine learning and statistics


If you work with data in any capacity, go ahead and do yourself a favor: download KNIME Analytics Platform right here. I could dive into a quick start tutorial or show off some of the more advanced capabilities, but it's honestly very intuitive to use. KNIME Analytics Platform is 100% free. Those features allow you to automate workflow deployment, execute workflows remotely from another service, and create an interactive hub for users. The ability to automate workflows makes KNIME Server Medium an attractive option.

New features and capabilities added to the Google Cloud Platform


The assistance for executing custom containers to train models on Cloud AI Platform has now become available to the customers. This capability enables users to come up with their own pre-installed ML framework and Docker container images to execute on the AI Platform. Google Platform has launched scale tiers, a set of predefined cluster specification based on a GCE VMs class, to streamline the process of choosing the right hardware configuration for the ML model. It enables customers to select the custom tier to determine machine configurations for master, worker, and parameter server.

Impact of AI on Work - Jobs Are Changing, MIT-IBM Watson AI Lab Says


IBM has always believed that 100% of jobs will ultimately change due to the impact of AI. Recent empirical research conducted by the MIT-IBM Watson AI Lab provides insights into the prediction and explains how it's going to happen. The joint research by Massachusetts Institute of Technology and IBM scrutinized the probable applications of Machine Learning in 170 million online job postings between 2010 and 2017 and came up with a report "The Future of Work: How New Technologies Are Transforming Tasks." The research examined the impact of Artificial Intelligence on employment and inferred that the result will be a significant decrease in the number of tasks. It additionally stated that work that would require "soft skills" would be given more focus on.

How would a robot or AI make a moral decision?


The first question is philosophical: a matter of moral theory. The second is technical: a matter of practical engineering. Philosophical analysis of the theoretical problem of practical action (moral theory) informs software design. Software design informs moral theory. As Lewin (1943) puts it: "There's nothing so practical as a good theory." My solution to the problem of right and wrong, succinctly stated, consists of five steps.

Who Is The Leader In AI Hardware?


A few months ago, I published a blog that highlighted Qualcomm's plans to enter the data center market with the Cloud AI100 chip sometime next year. While preparing the blog, our founder and principal analyst, Patrick Moorhead, called to point out that Qualcomm, not NVIDIA, probably has the largest market share in AI chip volume thanks to its leadership in devices for smartphones. Turns out, we were both right; it just depends on what you are counting. In the mobile and embedded space, Qualcomm powers hundreds of consumer and embedded devices running AI; it has shipped well over one billion Snapdragons and counting, all which support some level of AI today. In the data center, however, NVIDIA likely has well over 90% share of the market for training.

Grammarly raises $90M at over $1B valuation for its AI-based grammar and writing tools – TechCrunch


While attention continues to be focused on the rise and growing sophistication of voice-based interfaces, a startup that is using artificial intelligence to improve how we communicate through the written word has raised a round of funding to capitalise on its already profitable growth. Grammarly -- which provides a toolkit used today by 20 million people to correct their written grammar, suggest better ways to write things and moderate the tone of what they are saying depending on who will be doing the reading -- has closed a $90 million round of funding. Brad Hoover, the company's CEO, confirmed to TechCrunch that the funding catapults the company's valuation to more than $1 billion as it gears up to grow to more users by expanding Grammarly's tools and bringing them to more platforms. Today, Grammarly can be used across a number of browsers via browser extensions, as a web app, through mobile and on desktop apps, and through specific apps such as Microsoft Office. But in our current era of communication, the number of places where we write to each other is expanding all the time -- consider, for example, how much we use chat and texting apps for leisure and for work -- so expect that list to continue growing.