Hikvision has added to its DeepinView camera line with what it calls the Dedicated subseries. The company says this loads a batch of artificial intelligence-powered deep learning algorithms into each unit, which are of use in two distinct categories. The first is vehicle analysis, where cameras combine automatic number plate recognition with vehicle attribute recognition - including make, colour and direction of movement. Typical installations would be on city streets and at the entrances of car parks and buildings. Models in the second category have six switchable deep learning algorithms in one camera housing, including facial recognition, face counting and queue management.

With the popularity of machine learning and deep learning, several organisations and academia have started developing efficient tools and libraries. For instance, tech giants like Google, Microsoft, and Facebook have been heavily investing in building dynamic and robust deep learning models. When it comes to building deep learning models, Python is considered as one of the most suitable languages due to its plethora of tools and libraries available for performing machine learning tasks. In this article, we compared the two popular Python machine learning libraries, scikit-learn and Pylearn2. Before delving deep into the libraries, let's get through the basic definition first. Built on top of NumPy, SciPy, and Matplotlib, scikit-learn is a popular machine learning library in Python language.

This year is likely to be remembered for the Covid-19 pandemic and for a significant presidential election, but there is a new contender for the most spectacularly newsworthy happening of 2020: the unveiling of GPT-3. As a very rough description, think of GPT-3 as giving computers a facility with words that they have had with numbers for a long time, and with images since about 2012. The core of GPT-3, which is a creation of OpenAI, an artificial intelligence company based in San Francisco, is a general language model designed to perform autofill. It is trained on uncategorised Internet writings, and basically guesses what text ought to come next from any starting point. That may sound unglamorous, but a language model built for guessing with 175 billion parameters – 10 times more than previous competitors – is surprisingly powerful.

Developer Manuel Araoz has played a practical joke online to demonstrate the potential of artificial intelligence bots -- by having a bot write an article about itself. According to a July 18 post on Araoz's blog, AI development company OpenAI released GPT-3, the third generation of its language prediction model capable of creating "random-ish sentences of approximately the same length and grammatical structure as those in a given body of text." The blog entry provides practical information regarding how the technology could be used to impersonate well-known figures by simulating their writing styles -- for example, Araoz used it to create a fake interview with Albert Einstein. He predicted that the GPT-3 could potentially replace journalists, political speech writers, and advertising copywriters. The bot's predicted sentences were used for posts on the bitcointalk.org "There are lots of posts for GPT-3 to study and learn from.

A combination of machine learning with computer vision and computer graphics, 3D machine learning has gained traction due to the ongoing research in areas such autonomous robots, self-driving vehicles, augmented and virtual reality, which has given a boost to the concept. In this article, we list the top Python libraries for 3D Machine Learning. About: PyTorch3D is an open-source library for 3D deep learning written in Python language. The library is highly modular and optimised with unique capabilities designed to make 3D deep learning easier with PyTorch. PyTorch operators are implemented using PyTorch tensors for smooth integration of deep learning and 3D data and can handle mini-batches of heterogeneous data.

Well, that's where it started. I've still got a lot of work to do, but I'm working on a game that will be similar to but different than AI Dungeon. I want to build something similar but with my own flavor - maybe not as open-ended and with some other systems in place. I'm happy to announce that work has begun! Here's the latest video describing a bit more about the project and what's to come... Latest incarnation with plans and notes to turn it into a game of sorts... Here's a video from about a week ago before I had the idea to turn it into an RPG of sorts...

The developments in agricultural robotics, machine vision, and AI will drive a deep and far-reaching transformation of the way farming is carried out. Yes, today the fleet sizes and the total area covered by new robots are still vanishingly small compared to the global agricultural industry. However, this should not lull the players into a false sense of security because the ground is slowly but surely shifting. Robotics and AI are enabling a revolution in affordable precision, which will eventually upend familiar norms in agrochemical supply, in agricultural machine design, and in farming practices. This development frontier has the wind in its sails, pushed by rapidly advancing and sustainable hardware and software technology trends and pulled by structural and growing challenges and needs.

IDC considers the tight integration of hardware solutions provided by NetApp, NVIDIA, and Mellanox through the ONTAP AI reference infrastructure as a key reason for the implementation success of NISI's deep learning solution. This reference infrastructure was pivotal for seamless efficient setup, configuration, and troubleshooting throughout the deployment process. It was also a key differentiator for NISI, compared with other competitor solutions, and fundamental to its procurement decision-making processes and general satisfaction with the solution.

Your AI bartender will serve you now. This year is likely to be remembered for the Covid-19 pandemic and for a significant presidential election, but there is a new contender for the most spectacularly newsworthy happening of 2020: the unveiling of GPT-3. As a very rough description, think of GPT-3 as giving computers a facility with words that they have had with numbers for a long time, and with images since about 2012. The core of GPT-3, which is a creation of OpenAI, an artificial intelligence company based in San Francisco, is a general language model designed to perform autofill. It is trained on uncategorized internet writings, and basically guesses what text ought to come next from any starting point.

Semi-supervised learning improves the performance of supervised machine learning by leveraging methods from unsupervised learning to extract information not explicitly available in the labels. Through the design of a system that enables a robot to learn inspection strategies from a human operator, we present a hybrid semi-supervised system capable of learning interpretable and verifiable models from demonstrations. The system induces a controller program by learning from immersive demonstrations using sequential importance sampling. These visual servo controllers are parametrised by proportional gains and are visually verifiable through observation of the position of the robot in the environment. Clustering and effective particle size filtering allows the system to discover goals in the state space. These goals are used to label the original demonstration for end-to-end learning of behavioural models. The behavioural models are used for autonomous model predictive control and scrutinised for explanations. We implement causal sensitivity analysis to identify salient objects and generate counterfactual conditional explanations. These features enable decision making interpretation and post hoc discovery of the causes of a failure. The proposed system expands on previous approaches to program synthesis by incorporating repellers in the attribution prior of the sampling process. We successfully learn the hybrid system from an inspection scenario where an unmanned ground vehicle has to inspect, in a specific order, different areas of the environment. The system induces an interpretable computer program of the demonstration that can be synthesised to produce novel inspection behaviours. Importantly, the robot successfully runs the synthesised program on an unseen configuration of the environment while presenting explanations of its autonomous behaviour.