Lifeboat Foundation

Glow is an iconic interesting research about deep neural networks that can generalize with small training sets.

Since the early days of machine learning, artificial intelligence scenarios have faced with two big challenges in order to experience mainstream adoption. First, we have the data efficiency problem that requires machine or deep learning models to be trained using large and accurate datasets which, as we know, are really expensive to build and maintain. Secondly, we have the generalization problem which AI agents face in order to build new knowledge that is different from the training data. Humans, by contrast, are incredibly efficient learning with minimum supervision and rapidly generalizing knowledge from a few data examples.

Generative models are one of the deep learning disciplines that focuses on addressing the two challenges mentioned above. Conceptually, generative models are focused on observing an initial dataset, like a set of pictures, and try to learn how the data was generated. Using more mathematical terms, generative models try to infer all dependencies within very high-dimensional input data, usually specified in the form of a full joint probability distribution. Entire deep learning areas such as speech synthesis or semi-supervised learning are based on generative models. Recently, generative models such as generative adversarial networks(GANs) have become extremely popular within the deep learning community. Recently, OpenAI experimented with a not-very well-known technique called Flow-Based Generative Models in order to improve over existing methods.

Continue reading “This Neural Networkfrom OpenAI can Learn from Small Datasets” »

In your business you need to learn how to distinguish between chatbots and conversational AI, here are some tips on how to do that.

In recent years, roboticists have developed a wide variety of robots with human-like capabilities. This includes robots with bodies that structurally resemble those of humans, also known as humanoid robots.

Testing the performance of humanoid robots can sometimes be challenging, as there are numerous measures to consider when trying to determine their applicability in real-world scenarios. Two features that are particularly important for humanoid robots are posture control and balance, as these robot’s body structures can sometimes make them prone to falling or stumbling, especially in complex environments.

Researchers at Technische Universität Berlin and the University Clinic of Freiburg recently created a system to evaluate the posture control and balance of both humans and humanoid robots. This system, presented in a paper pre-published on arXiv, is designed to assess balance and posture control of robots or humans as they perform different movements on a moving surface.

China’s going all in on deep learning. The Beijing Academy of Artificial Intelligence (BAAI) recently released details concerning its “Wu Dao” AI system – and there’s a lot to unpack here.

Up front: Wu Dao is a multi-modal AI system. That means it can do a bunch of different things. It can generate text, audio, and images, and, according to Engadget, it can even “power virtual idols.”

The reason for all the hullabaloo surrounding Wu Dao involves its size. This AI model is huge. It was trained using a whopping 1.75 trillion parameters. For comparison, OpenAI’s biggest model, GPT-3, was trained with just 175 billion.

Last month, self-driving technology company TuSimple shipped a truckload of watermelons across the state of Texas ten hours faster than normal. They did this by using their automated driving system for over 900 miles of the journey. The test drive was considered a success, and marked the beginning of a partnership between TuSimple and produce distributor Guimarra. This is one of the first such partnerships announced, but TuSimple may soon have some competition from another big player in the driverless vehicles game: Alphabet Inc. subsidiary Waymo.

Yesterday, Waymo announced a partnership with transportation logistics company JB Hunt to move cargo in automated trucks in Texas. The first route they’ll drive is between Houston and Fort Worth, which Waymo claims is “one of the most highly utilized freight corridors in the country.”

At around 260 miles long, much of the route is a straight shot on Interstate 45. The trucks will have human safety drivers on board who will likely take over some of the city driving portions, but the goal is to use the automated system as much as possible. A software technician will be on board as well, which makes sense given software will be doing the bulk of the driving.

Floorplanning is the process by which an integrated circuit is designed using a top-down view. Rather like the architectural plan of a home, garden, and walkways, each of the major functional blocks is placed in a schematic representation that provides a guide for where everything needs to be. This layout can include transistors, capacitors, resistors, wires and other components, all packed into extremely tiny spaces.

Determining the optimal configuration for processing speed and power efficiency is a detailed and lengthy task, involving many iterations. It can often take weeks or even months for expert human engineers. Attempts to fully automate the process have been unsuccessful.

However, researchers from Google have this week reported a new machine-learning approach to floorplanning. Not only does it reduce the design workload to a matter of hours, it also results in chips with superior designs.

Using the full system, farmers could reduce costs by 40% and chemical usage by up to 95%.

Small Robot Company (SRC), a British agritech startup for sustainable farming, has developed AI-enabled robots – named Tom, Dick and Harry – that identify and kill individual weeds with electricity. These agricultural robots could reduce the use of harmful chemicals and heavy machinery, paving the way for a new approach to sustainable crop farming.

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Could this be the future of pizza?

Simply log onto the pizza maker’s mobile application, input the number and type of pizzas you want, and the machine will do the rest of the work.

It is called the “city brain”, an artificial intelligence system that is now being used across China – only megacities could afford them before – for everything from pandemic contact tracing to monitoring illegal public assemblies and river pollution.

Authorities at all levels are now using AI for everything from pandemic control to monitoring illegal public assemblies.