Let’s start from the beginning.

We start from a zygote, a cell formed by the fusion of the male and female gamete (gametes are the male and female with only 23 chromosomes, unlike the regular cells with 46 chromosomes). After the fusion, the zygote has a complete set of 46 chromosomes. In case you forgot, genes are segments of DNA, and chromosomes are the cells’ structures that contain the genes – hundreds to thousands of genes. So zygote contains the DNA – the complete DNA that defines us. DNA is a fascinating multidimensional code ever known to humankind. It is not your typical software function. It has the encoded instruction set for complete development.

From one cell, the zygote will soon become multicellular, as each cell will undergo mitosis. The cells divide, and the new cells have identical copies of the DNA as the original cells. The cells rearrange themselves spatially to form three layers that differentiate into different organ systems. The cells multiply and change. They produce cells that become white blood cells, red blood cells, nerve cells, eyes, liver, fingers, toes, hair, heart, skin, etc. A cell becomes a palm, an adjacent cell becomes a finger, and another becomes a nail. Not only does a cell know what to do, but it also knows what neighboring cells should do. The DNA instruction set is controlling this.

But to me, the most fascinating thing is the learning that gets developed in the brain. The cells give rise to all the neurons in the brain stem so that a newborn knows what to do – kicking, grasping, crying, sleeping, rooting, feeding, etc. The correct synaptic connections, the correct synaptic weights, the correct inhibitors, the correct circuits! Nothing is random – a well planned execution of the DNA code.

The code in DNA builds these initial learning/inference models in the brain.

Initial learning is coming from DNA … transfer learning. But the kicking, grasping, crying, sleeping, rooting, feeding are not the only elements of learning that a newborn is born with. There is lot more learning in the brain – the unconscious bias – the things that we will discover about the baby as the baby grows.

Let me ask you this. What is something that you know today but you have never learned?

When we use the term DNA, it may sound that learning is influenced only by the parental genomes, but it is a lot more interesting. Along with the genome, we also have an epigenome.

The epigenome is a series of chemical tags (called epigenetics markers) that promote or repress the expression of genes without altering the genome. These epigenetic modifications are added in response to the physiological or environmental stimuli our cells receive. Diet, stress, habits, learning, exercise are a few examples that can permanently alter the epigenetic profile of an individual. Researchers use the term “imprinted genes” to refer to genes influenced by the epigenetic markers. The DNA is not modified, but these epigenetic modifications influence the instruction code contained in the DNA.

Epigenome is the software layer on top of the DNA code.

The learning in the newborn is not just coming from the DNA i.e. defined by the parental genome – the default learning of a baby is influenced by the epigenetic modifications too. These markers fully influence the zygote development.  Our learnings, Karma, habits, environment, and lifestyle influence what our newborns will know by default. A child may have the same habits as the father. A child may know things the mother spent years learning. All these sentences make sense biologically. The genome and epigenome take part in that initial learning. It is not just the kicking, grasping, crying, sleeping, rooting, and feeding; unconscious bias, aptitude, habits, and a lot of learning is transferred from generation to generation.

Are people responsible for the (unconscious) actions based on the learning/behaviors they are born with? That is a debate we can discuss another time.

Now, let me ask you this. What are the epigenetics-specific learnings that you think came from your parents?

Regardless, this is very interesting for AI/ML engineers as brain development from the genome and epigenome offers a cue to storing model, transfer learning and incremental model change mechanisms. Brain is fascinating and to build real intelligence, we need to dig a lot deeper – not just neurons but also genome and epigenetics. Answers are in the nature, we just need to look closer.

There are two kinds of people in the world — those who divide the world into groups of two and those who don’t. And, here is, now, my take on the AI. There are two kinds of AIs — those that are built using BIG DATA and others that are NOT.

The BIG DATA AI

In simplistic terms, the BIG Data powered AI models “learn” without being explicitly programed to do so. The learning is basically a process that examines a large set of samples and creates a formula using a large number of parameters many of them seem indistinguishable to the naked eye. It is a time-consuming trial and error process where the parameters are continually adjusted until training data with the same labels consistently yield similar outputs. A classic example of this is using a data set that has 50000 images of digits (0 to 9) and the AI model learns to recognize the digit.

The BIG DATA AI is all about “mathematically recognizing” incredibly subtle patterns within the mountains of data.

Using this “pattern recognition technique” you can now “provide” an answer, a decision or a prediction to an input data that you have never seen before. It is a great tool but is unlike the human brain in basic learning. These algorithms require mountains of data and high CPU/GPU power to train. It has no common sense, conceptual learning, creativity, planning, human-like intuition, imagination, cross-domain thinking, self-awareness, emotions, etc. It has trouble when it comes to extreme edges of data for which it had limited samples.

BIG DATA AI is basically an optimizer based on a large volume of data for a very specific vertical single task.

The MIN DATA AI

In MIN DATA AI, the AI models “learn” with minimal data that is needed to learn. A formula is indeed created however with minimal data. A classic example of this is how some people say they never forget a face even if they met in passing, or, how you recognize a taste even if you have experienced it only once before.

MIN DATA AI is closer to how we learn and do problem-solving. I don’t need to drink 10,000 OLD FASHIONED (cocktail) to tell the drink in my hand is old fashioned (although may be in my case I already have) or to recognize that the old fashioned is from HaberDasher San Jose or NOT (and, yes, HaberDasher is a great place). With this AI, you will be able to make bets even though data says something else. Your learning can be super fast.

Here is how you can start your journey for MIN DATA AI:

1. Stop looking for BIG DATA. Like any other addiction, it will be difficult to cope with BIG DATA addiction but you have to do it.
2. Focus more on algorithms.
3. Innovate how to create BIG DATA from MIN DATA.

In next 3 years, AI will rely less on BIG DATA and more on MIN DATA. This new MIN Data approach of AI will enable a whole new set of use cases that seemed unsuited before.

MIND is perhaps MIN D(ata)

Future of AI making devices, processes and automation intelligent (and not just high confidence level decision making based on BIG Data) is not far. 

• Navier–Stokes equations are the fundamental basis of almost all Computational Fluid Dynamics (CFD) problems. These are extremely useful to model the weather, airflow around an airplane wing, ocean currents, water flow in a pipe, and the analysis of pollution. But these cannot be used in real-time due to the time they need for computation.
• Fully describing an arbitrary many-body state in quantum mechanics requires an exponential amount of information. While simulating a quantum system with 30 qubits requires just tens of gigabytes, simulating 300 qubits requires more bytes than the number of atoms in the observable universe! Even the state-of-the-art approximation methods for quantum mechanics such as Hartree-Fock Theory (HF) and Density Functional Theory (DFT) can take a long time: hours, days, or even weeks to compute. Learn more about QuBits.
• Even with the computing power available today, simulations or real-time analysis for acoustic transmission or absorptions in buildings can take a long time.
• Computer simulation of electrons in the potential of atomic nuclei is the workhorse of modeling material properties such as phase stability, mechanical behavior, and thermal conductivity. However, these simulations are limited by their computational cost.

Deep learning has the potential to break through this limitation.

Imagine creating a deep learning model by constructing a dataset that covers the entire physically relevant set of configurations for the problem and then just using the model to completely bypass costly calculations in the future.

You can use the predictive power of deep neural networks to cut the computation time down to a couple of seconds.

Yes, you will do complex simulations once, but only once! Once you have built your dataset, decided the fitting methodology such as a simple FFN (feed forward neural network), RBM (a restricted-Boltzmann-machine) or any other neural network architecture, your model can serve as a template for all future work!

To me, this is one of the best uses of deep learning to build an explanation without too much computation!

People often think of AI for boosting growth by substituting humans, but actually, huge value is going to come from how humans will use AI. This is yet another perfect example how deep learning will help us advance more.