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How to Make Sense of the Reinforcement Learning Agents? What and Why I Log During Training and Debug -


Based on simply watching how an agent acts in the environment it is hard to tell anything about why it behaves this way and how it works internally. That's why it is crucial to establish metrics that tell WHY the agent performs in a certain way. This is challenging especially when the agent doesn't behave the way we would like it to behave, … which is like always. Every AI practitioner knows that whatever we work on, most of the time it won't simply work out of the box (they wouldn't pay us so much for it otherwise). In this blog post, you'll learn what to keep track of to inspect/debug your agent learning trajectory. I'll assume you are already familiar with the Reinforcement Learning (RL) agent-environment setting (see Figure 1) and you've heard about at least some of the most common RL algorithms and environments.

Basic concepts in Machine Learning


Artificial Intelligence involves all the characteristic operations of the human intellect and performed by computers, such as planning, language understanding, recognition of objects and sounds, learning and problem solving. Very interesting is the relationship between AI and IoT (Internet of Things) similar to that between brain and human body: our body through i various sensory inputs such as sight and touch, can recognize certain situations by performing the corresponding actions driven by our brain. Similarly in the IoT which, through sensors connected in the field, it sends a set of information to a guided control system by Artificial Intelligence that takes the appropriate decisions and eventually activates the actuators for controlling various movements (for example robot arms). Machine learning, on the other hand, is a way to implement Intelligence Artificial, while in-depth learning or Deep Learning, is one of many approaches related to machine learning. Machine learning is an application of AI that enables systems to learn and improve from experience without being explicitly programmed.

Agriculture embraces artificial intelligence


Graybeards may remember the thrill they felt when pencil-laden math calculations moved warp speed ahead into the calculator age. These days, artificial intelligence (AI) promises to bring the same heat to agriculture that it did to math classes decades ago. Artificial intelligence is a technology that includes several subsets such as machine learning, says Rania Khalaf, Inari chief information and data officer. "Machine learning enables computers to mathematically predict outcomes or make classifications by finding patterns in large amounts of data," she says. "It then learns to update these patterns or classifications over time as it sees new data."

20 Artificial Intelligence Project Ideas for Beginners to Practice


The projects described above are by no means an exhaustive list. AI is an incredibly vast field, and with some creativity and technical know-how, you will be able to create some fantastic artificial intelligence software projects to showcase on your portfolio. With the democratisation of AI, it has become increasingly easy to build AI models to solve business problems across diverse business domains. High-level libraries like FastAI and open-source pre-trained models have made AI accessible to everyone. As long as you have an intermediate understanding of machine learning and programming, you can build models to fit various use-cases.

How To Get Deep Learning Results Without Spinning New Wheels.


In machine learning, to train a neural model, one typically needs a lot of data. This is challenging for many clients, as access to that data isn't always easy- this is when transfer learning comes in handy. Transfer Learning: Reusing a previously trained model on a new problem, especially when the new problem has a similar structure or features to that of the target domain. This is particularly valuable in the field of data science, as most real-world situations do not require millions of labeled data points to train complicated models. Blinx AI's transfer learning feature is one of the best ways to speed up and reduce the cost of training your AI model.

Touch sensing: An important tool for mobile robot navigation


In mammals, the touch modality develops earlier than the other senses, yet it is a less studied sensory modality than the visual and auditory counterparts. It not only allows environmental interactions, but also, serves as an effective defense mechanism. The role of touch in mobile robot navigation has not been explored in detail. However, touch appears to play an important role in obstacle avoidance and pathfinding for mobile robots. Proximal sensing often is a blind spot for most long range sensors such as cameras and lidars for which touch sensors could serve as a complementary modality.

TechScape: Enter the multiverse – the chat-room game made of AI art

The Guardian

The Bureau of Multiversal Arbitration is an unusual workplace. Maude Fletcher's alright, though she needs to learn how to turn off caps lock in the company chat. But trying to deal with Byron G Snodgrass is like handling an energetic poodle, and Phil is a bit stiff. Byron G Snodgrass is an energetic poodle. A peace lily, I think.

Brain mapping in mice may explain why pain makes us lose our appetite

New Scientist

The link between chronic pain and a loss of appetite may finally be understood – in mice at least. Zhi Zhang at the University of Science and Technology of China in Hefei and his colleagues injected mice with bacteria that provoke chronic pain. Ten days later, these mice were eating less frequently and for shorter periods of time compared with control mice that had been injected with saline. When the first group of mice were later given pain medication, they ate normally, the researchers wrote in a paper published in Nature Metabolism. To better understand the neuronal activity responsible for this change in behaviour, the researchers analysed the brains of the first group of mice while the animals were in chronic pain.

Driverless car projects: our pick of 10 favorites - DesignWanted : DesignWanted


There was a time when no one could imagine a driverless car would ever exist. But gradually, what we once thought was impossible has become a reality. The first autonomous cars are now commercially available! Although Leonardo da Vinci developed the self-propelled carriage in the 15th century, it was in the 20th century that the concept was realized. When Google announced in 2009 that it would start researching unmanned cars, the idea became even more attractive. Currently, several well-known companies are looking into developing semi-autonomous and fully driverless cars, which could result in significantly fewer traffic accidents.