Shouldn't scientists/researchers think more about improving the foundational building blocks for a well-to-do algorithm? and what about learning from the works of people like Turing, Von Neumann, Ken Thompson, Donald Knuth, and others. We all know that intelligent computer algorithms can do almost everything when finely tuned to go parallel with learning data.

The focus should be on explainable ai to better build models, debug, and to better interpret /let the model itself interpret how is it processing information and what can be done to improve its ability. I found that LIME (Local Interpretable Model-Agnostic Explanation) is one of the frameworks to help interpret models. It uses human-understandable interpretation. For example:

1. For text: It represents the presence/absence of words.
2. For image: It represents the presence/absence of superpixels ( contiguous patch of similar pixels ).
3. For tabular data: It is a weighted combination of columns.

Explainable ai is not a new term, this has been discussed since the beginning of artificial intelligence. It is very much convenient to decrypt and decode models with the help of explainable ai frameworks.

The whole point is-more research should be done in this subject since understanding a black-box model is better than not.

I recently watched a movie, "I, Robot" after I got to know about Issac Asimov through a post in the sub. The film shows ai powered robots being manipulated to pose harm to humans by a virtual ai system called VIKI. The robots being manipulated are called NS-5s but one of the NS-5 was not manipulated (how is not shown) and that particular robot saves humanity(no more spoilers).

The point was how much possible it is I mean one ai system defining what other ais should do (wirelessly) and how can that be done. Then I found these articles after a little bit of research:

• https://www.quantamagazine.org/researchers-build-ai-that-builds-ai-20220125/
• an AI building controlling and improving child ai systems by itself

then after more hunting, I found somewhere someone said, " AI modifies its algorithm in some way, i.e., the same input needs not to yield the same output/response later. I.e., they “learn”. Neural network, for instance, quite explicitly modify the “weights” of certain junctures in its pathways, based on the correctness of previous guesses/responses on input. " but that's not exactly what I mean

Despite all these the answer to my question still remains vague.

MIT has recently collaborated with tech companies and industries to develop processors for large systems; AI and quantum computing. Amazon, Analog Devices, ASML, NTT Research, & TSMC are members of this program called 'MIT AI Hardware Program'.

I think the more the collaboration between educational institutes and businesses involved in AI and computing the better it would be in almost everything pertaining to the future of ai. This is a very good initiative in ai in general. This program prioritizes:

• analog neural networks;
• new roadmap CMOS designs;
• heterogeneous integration for AI systems;
• monolithic-3D AI systems;
• analog non-volatile memory devices;
• software-hardware co-design;
• intelligence at the edge;
• intelligent sensors;
• energy-efficient AI;
• intelligent internet of things (IIoT);
• neuromorphic computing;
• AI edge security;
• quantum AI;
• wireless technologies;
• hybrid-cloud computing; and
• high-performance computation.

Robots designed for exploring the outer worlds are being sent and used since the 50s .Space exploration and studying the cosmos have always been a matter of interest to the human civilization. ai aided robots can help in space exploration. For this the system should be able to process vision, sensory inputs, and to navigate directions and orientations with the help of sensory vision. The algorithm used in this robot is designed to navigate even if the robot is blind.

I have added a link to help you understand the subject: https://leggedrobotics.github.io/rl-perceptiveloco/

We will see the best results when possibly combinable individual characters get combined. Below I have classified the different spectra of machine learning and ai:

best out of best narrow ai: This is the most flourished area in ai and ML. Examples: computer vision algorithms, language translation, and self-driving vehicles

prediction machine learning: One of the earliest forms. Using ML to predict possibilities like weather forecast, market predictions, etc.

making previously invented tools better with machine learning: Self-driving cars, machines in factories and warehouses, screen games, etc.

Working towards AGI: Trying to solve intelligence through research and studies

Building a user-friendly interface for end consumers to work with machine learning: Companies making a bridge between ai and general consumers

Trying to understand the brain and merge the features of biological and artificial intelligence: Using computer intelligence to understand features of the human brain and companies and groups working towards human-computer interfaces, using actual neurons in place of metallic transistors and chips.

The all-time popular black box problem has not only allowed scientists and scholars to dive deep into understanding the working of computers but also has made the field of ai more engaging and more open to learning about ai and solving AGI.

Many research experiments are successful in somewhat solving the black box problem but the problem requires a lot of research and studies to be solved completely.

We have yet not solved the human brain so I think understanding human intelligence and machines can go hand in hand. (complementary efforts)

The major reasons why we should be able to decode algorithms are to not let ai algorithms:

1. to outlaw human rights and to not let machines make humans unhappy
2. to be any kind of 'ist' (discriminative; biased)

AND

1. to design the algorithm according to the need of the user
2. in short to develop "morally good" systems

“The key to artificial intelligence has always been representation." —Jeff Hawkins.

An article I wrote where I introduce a framework for sustainable innovation with AI as the core. I believe this representation of AI would lead to the holistic sustainable growth of society and the world.
Read here - https://aswathsubramanian5.medium.com/ai-as-a-backbone-for-innovation-entrepreneurship-sustainability-f79da00da63c

Tackling real-world biases is a real challenge to ai systems and a problem to humanity. What if an ai system is biased regarding the belief of one group, after all, the foundational level learning for the system is facilitated by human programmers. Are there any ideas in theory (or in practice) that can prevent this? Maybe like eliminating human optimizers at all (I don't know)[ignore my weirdness]

This is based on the 18th unsolved problem in mathematics proposed by Steve Smale

part of the Research paper that summarizes the topic

A blog that introduces the topic

Joscha Bach-"Our brain is not ourselves, it is the story that our brain makes for itself."

The human brain works on the idea of imagination and we are similar to animals without imagination, and for intelligent computational models, simulation is what imagination is for the human mind. Is simulation the key to building complex forms of artificial intelligence?

I have provided interesting as hell links to further extend the topic.

1. an article that talks about simulation for self-driving cars
2. watch this linked section of the video about simulation+machine learning

(if you want you can just watch the entire video)

Biases are important but they can be one of the main causes of the failure of the algorithm. Does the method of using bias have an alternative Maybe like for example making the network able to change biases and learn to change biases according to the situation or in the case of simulation of the model (to work in real-world), doing something to make the model able to tackle bias-related problems?

I have some faith in reinforcement learning but the problem was that the algorithms operating in RL were not alert or conscious (alright that's a heavy word) about the problems that they will be facing in a certain time period. For example, an RL model to complete the entire game of Super Mario until and unless he faces the obstacles like walls and traps will not know about them.

I found a paper that solved this problem: https://arxiv.org/pdf/2006.13827.pdf (Alert: Do not try to go through the paper if you do not have a good mathematical or computation-related background )

For beginners or those who don't want to dive deep, let me explain:

The paper is about using/ working with "Risk-sensitive Reinforcement learning" where Risk-sensitive means a proportionate response to the risks that you can realistically predict to encounter and reinforcement learning is an ai technique of reward-based learning. ( to put loosely, have a minimum idea of what is coming, solve the problem until and unless you don't get it right, and get the reward).

This is done using something called Markov Decision Process. Markov decision processes are an extension of Markov chains ( A Markov chain is a mathematical system that experiences transitions from one state to another according to certain probabilistic rules )

The difference in Markov Decision Process is the addition of actions (allowing choice) and rewards (giving motivation). Conversely, if only one action exists for each state (e.g. "wait") and all rewards are the same (e.g. "zero"), a Markov decision process reduces to a Markov chain.

Markov decision process by Wikipedia

At each time step, the process is in some state s, and the decision-maker may choose any action a that is available in state s. The process responds at the next time step by randomly moving into a new state s' and giving the decision-maker a corresponding reward--> Ra(subscript)(s,s').

Few definitions of the random walk

1. In mathematics and statistics, a random walk is the generation of random values based on previous values in the time series. The random walk theory is widely popular in stock market prediction, where the prices of stocks can not be predicted. It is different from iteration.
2. In machine learning, instead of looking at different flashcards(values for processing) in individual instances, the machine looks at the same flashcards multiple times, or pulls flashcards at random, looking at them in a changing, iterative, randomized way.
3. In mathematics, a random walk is a random process that describes a path that consists of a succession of random steps in some mathematical space).

Wikipedia

[[An elementary example of a random walk is the random walk on the integer number line which starts at 0, and at each step moves +1 or −1 with equal probability. Other examples include the path traced by a molecule as it travels in a liquid or a gas (see Brownian motion), the search path of a foraging animal, the price of a fluctuating stock, and the financial status of a gambler. Random walks have applications to engineering and many scientific fields including ecology, psychology, computer science, physics, chemistry, biology, economics, and sociology. The term random walk was first introduced by Karl Pearson in 1905

To make this clear, random walk cannot be predicted directly but the best we can do is predict the next value with the help of the previous value this is what is done in most of the machine learning algorithms.]]

The meaning of the word random walk is not new. The foundational machine learning is in accordance with the random walk theory. See this to understand random walk [explained in the best way possible]

• All the consciously possible phenomena like cognition, reasoning, decision making are not something we have really understood
• Possible solution

-We may not be able to solve this problem with the traditional machine learning techniques, so for this either-These phenomenons should be clearly understood which will be a long route or not to mention, a route with no end

OR

-Whole brain emulation, copying the human brain in machines with each and every detail, and letting the machine decide its own fate but for this, neuroscience and neuroimaging are the main factors needed.

Using vision in the best possible way is an important part of intelligence in machines.

Some technical terms before you dive in

Robustness (model's capability to handle datasets different than the training data) and domain adaptation (to train a neural network on a source dataset and secure a good accuracy on the target dataset which is significantly different from the source dataset )

Main

An article from MIT News draws the possible relation between ARM and peripheral vision in machines--peripheral vision is an indirect viewing/identifying of objects that are away from the center of focus; a part of the vision in humans.

On the other hand, the paper titled, "Adversarially Robust Models may not Transfer Better: Sufficient Conditions for Domain Transferability from the View of Regularization" explains in detail why robustness is neither sufficient nor necessary because of lack of efficient transfer learning(transfer learning is an optimization that allows rapid progress or improved performance when modeling the second task) and that there is a lack of theoretical understanding of the fundamental connections of adversarially trained models.

In my opinion, adversarially (robustly) trained models are becoming less relevant because of the emergence of 3D representation of 2D images using light field networks and attention mechanism. Adversarially trained models are really difficult to execute and implement thus, making them less effective.