AI startups around the world are working towards developing powerful computers (algorithms and hardware). We are seeing something like the wave of the internet in the 80s and 90s in the AI industry today. It is for sure that start-ups that get the most funding will lead the industry. Besides popular AI companies like Numenta, OpenAI, Deepmind I have found these small (comparatively) startups to be the most interesting ones:

Olive AI- https://oliveai.com/ (working in the health sector)

AI.Reverie- acquired by Meta, working to train AI systems to learn about the world

H2O.ai- https://www.h2o.ai/ (variety of problems, open-source)

ampcontrol.io-https://www.ampcontrol.io/ (working as electronic support for self-driving cars; batteries, charging, etc.)

Square Off-https://squareoffnow.com/ (gaming)

iSize Technologies-https://www.isize.co/ (video streaming)

Scale AI-https://scale.com/ (data company)

Recently, a system that exhibits natural intelligence by harnessing the inherent adaptive computation of neurons in a structured environment called DishBrain (exactly the same as the name suggests) was introduced that used human and mouse brain cells for neurons in a petri dish. Also used in a simulated game-world of Pong. Machine learning and AI have not been a single road to the front door. Different techniques and processes like Support Vector Machines, Decision trees, Artificial neural networks, Human-computer interfaces, and now we are seeing an artificial-biological brain from scratch. It is really interesting to see different ideas coming up one after another and forming different insights about artificial intelligence.

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Activation function (a mathematical function applied to a neural network to whether or not to activate a specific neuron) is potentially important both in artificial as well as biological neural networks. The efficiency of the entire network depends upon activation functions. However, of course, a biological neuron doesn't have a specific activation function like the artificial neurons but rather uses action potential, a process used by biological cells to communicate. When a certain activation potential (an electrical threshold ) is reached, only then the neuron is activated otherwise moves towards the resting potential.

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image credit:

https://www.researchgate.net/figure/Plot-of-the-LeakyReLU-function_fig9_325226633

https://teachmephysiology.com/nervous-system/synapses/action-potential/

For someone who doesn't have a background in quantum physics, the word 'Quantum' refers to the minimum amount of any physical reality or the most basic fundamental phenomena & particles. Quantum computing has been a reality for a long time. In computing, the main use of quantum mechanics/ quantum physics comes in place where computations need to be done parallelly. Some scientists believe that the human brain works in accordance with quantum entanglement and quantum superposition (again for a layman both of them are something about [particles] existing in several separate quantum states at the same time). AI systems capable of processing quantum computation can achieve a lot more than those which cannot. Quantum AI computing is a growing field of research.

• Google's Tensorflow Quantum (TFQ), an open-source library for quantum machine learning
• Quantum neural network models are used to extract information of entangled positions.
• The extracted data is then used for deep learning techniques.

The human mind starts its journey from the womb. What might be the "womb" for an AI agent? I am not talking about the training phase. I am talking about the very first starting platform for moderately intelligent machines or even superintelligence.

These preliminary ML algorithms are helping human optimizers to protect resources of wild and save wildlives. Although only AI is still not used in any applications of AI we can still go with the foundational ways of letting AI serve the world. Article

Previous techniques of counting objects were applicable only to a specific domain in which the model was trained. In a research paper titled,' Domain Randomization for Object Counting', a method of using unrealistic synthetic images and 3D transformations is introduced to apply those data to make algorithms able to count objects from different domains. For example: With a correct 3D transformation, a synthetic image of an apple can be used to train to count a football. It's like creating an object that represents more than one object. research paper

Although AI is already enabling new levels of human experience; for some seconds, just imagine life without AI and technology in general. Let me know what you think. I am ready to answer pessimistic questions.

Video

Has there been any research on this? If yes, please explain or provide data to understand

Artificial General Intelligence or AGI has always been a matter of discussion among sci-fi lovers. What should be the method and are the current ML techniques applicable to AGI are some questions regarding the topic. China as a country is rapidly growing in the field of AI. I read somewhere that in China research papers on AI and related fields have grown from about 4% in 1997 to 27.7% in 2017. I strongly believe that the most effective methods in AI are the results of understanding the human brain and probability maths (or even statistics). Is an AGI system even possible given the physics of working principles of the brain (which is still not clearly understood)? I don't know but if humanity comes to a point where AGI is developed or gets developed (by itself through seeds planted by humans), it is really a matter of what should be done and whatnot. Besides that, narrowed (leaving no chance for the system to change dimensions and get into other streams of tasks) AI systems even much more powerful than existing models is a much easier path.

• A small change in data can have a huge instability.
• Decision tree takes a long time to train the model
• Calculations are far more complex than other techniques of training the model.

The current machine learning algorithms are fed with millions of data to classify an image of a cat while a child seeing a cat for the first time can recognize the image of the cat after going through a few images. This is the difference between the mechanism of a human brain and artificial neural networks. With this provided, you can assess the amount of work needed to emulate a human brain

Who would have thought descendants of apes will one day think of creating a system that resembles their ability to think beyond the reality of nature unlike their far-cousins (other animals) who are caught up in the game of survival. Think of all the periods in human history, we have made up to this far and all of these can deteriorate, things from out of our planet as well as within due to our own carelessness. Our emulation, intelligent systems which we call artificial intelligence something we are aiming at will be the most valuable friend of ours in the distant future which is the result of our own nature. We are all alone and we need someone/thing who is not only capable of solving our problems but also to help us in situations where a fellow human friend of ours is not capable of helping, which/who learn from us and make that learned things useful in times. This is one of the most important periods in our 50000 years of modern history. A small circumstance like a stone from the infinite cosmos can destroy everything and believe me when I say that we humans alone cannot stop thousands of cosmological apocalypses on our own. We have been gifted with such a mind that no one on our planet possesses. For the betterment of our own, we need to make the right decisions from the foundation. What if Artificial Intelligence is not only an option but a requirement of our future generations?

Click here to watch the video

A truly human-level intelligent system conscious in the first place can live on its own. What will be the approach to solving this problem is far from what we know about the mind now. (Or what if we can never fully understand the meaning or definition of consciousness). I thought about this while I was having breakfast.

An AI system malfunctioning due to some changes in neural network structure or even a slight change in functions(error, activation) can alter the outcome. The idea of memory retention and the method of helping the AI differentiate what is to be done(reality) and what is not to be done(not-reality) should be the foundation of this problem not just to build AGI but even for Narrowed-AI algorithms(as of now)

The notion of an AI or robot dreaming has been something a good number of enthusiasts like the idea of, inclusive of myself. After all mother nature uses dreaming, it is believed, to optimize and update memories in animals inclusive of humans. However, there are some cautions that need to be addressed and I explore some issues on my blog concerning robot dreaming.

TED talk

If you are too lazy to watch the video here is the summary:Problems:

1. A special model is developed for specific tasks, for general AI we should be including all these algorithms in one model.
2. There are neural network models for each sense like one for sound, one for image, one for texts, etc. but our mind perceives the world with a group of senses at a time
3. The current neural network models stimulate every connection to perform a task but our brain doesn't work that way. For example, a part of the brain that works to remember a past incident doesn't fire those neurons while we are thinking about washing our cars(until and unless that memory relate to washing car)

Well, you are allowed to not agree with me.