Considering the lengthening timelines for deep machine learning and AI projects to fight against COVID-19 the interest in transfer learning has grown significantly. Transfer learning for deep machine learning is the process of first training a base network on a benchmark dataset (like ImageNet), and then transferring the best-learned network features (the network’s weights and structures) to a second network to be trained on a target dataset. This idea has… Read More »Transfer Learning A Step by Step Easy Guide
Key Artificial Intelligence Projects to Fight Against COVID-19 Dr. Amit Ray Compassionate AI Lab In our Compassionate AI Lab, we broadly classified our fight against COVID 19 Artificial intelligence (AI) based research projects into six groups. They are AI for COVID vaccine development, AI for COVID drug discovery, AI for COVID diagnosis, AI for COVID testing, AI for COVID growth rate forecasting, and AI for social robots. Researchers across the… Read More »Artificial Intelligence to Fight Against COVID-19
Artificial Intelligence (AI) has provided remarkable capabilities and advances in image understanding, voice recognition, face recognition, pattern recognition, natural language processing, game planning, military applications, financial modeling, language translation, and search engine optimization. In medicine, deep learning is now one of the most powerful and promising tool of AI, which can enhance every stage of patient care —from research, omics data integration, combating antibiotic resistance bacteria, drug design and discovery to diagnosis and selection of appropriate therapy. It is also the key technology behind self-driving car.
However, deep learning algorithms of AI have several inbuilt limitations. To utilize the full power of artificial intelligence, we need to know its strength and weakness and the ways to overcome those limitations in near future.
Now, AI support messaging apps, and voice controlled chatbots are helping people for deep space communications, customer care, taking off the burden on medical professionals regarding easily diagnosable health concerns or quickly solvable health management issues and many other applications. However, there are many obstacles and number of issues remain unsolved.
Even with so many success and promising results its full application is limited. Mainly, because, present day AI has no common sense about the world and the human psychology. Presently, in complex application areas, one part is solved by the AI system and the other part is solved by human – often called as human-assisted AI system. The challenges are mostly in the large-scale application areas like drug discovery, multi-omics-data integration, assisting elderly people, new material design and modeling, computational chemistry, quantum simulation, and aerospace physics.
This article is focused to explain the power and challenges of current AI technologies and learning algorithms. It also provides the directions and lights to overcome the limits of AI technologies to achieve higher levels learning capabilities.
Quantum Machine Learning and the Deep Intelligence Frameworks – The 10 Key Properties
Dr. Amit Ray, Compassionate AI Lab
In this article, we discussed the 10 properties and characteristics of hybrid classical-quantum machine learning approaches for our Compassionate AI Lab projects. Quantum computers with the power of machine learning will disrupt every industry. They will change the way we live in this world and the way we fight diseases, care for old people and blind people, invent new medicines and new materials, and solve health, climate and social issues. Similar to the 10 V’s of big data we have identified 10 M’s of quantum machine learning (QML). These 10 properties of quantum machine learning can be argued, debated and fine tuned for further refinements.
The field of hybrid classical-quantum machine learning has been maturing rapidly with the availability of the prototype universal gate-model quantum processors of IBM, Google, and Rigetti as well as more sophisticated quantum annealers of D-Wave systems. Moreover, the availability of various high-performance computing (HPC) simulation of quantum circuits facilities improving the possibilities of exploring the power of QML in various application areas. Quantum hardware dedicated to machine learning are also becoming reality. They too can provide much faster processing power than a general-purpose quantum computer.
The compassionate AI lab is currently developing a hybrid classical-quantum machine learning (HQML) framework – a quantum computing virtual plugin to build a bridge between the available quantum computing facilities with the classical machine learning software like Tensor flow, Scikit-learn, Keras, XGBoost, LightGBM, and cuDNN.
Presently the hybrid classical-quantum machine learning (HQML) framework includes the quantum learning algorithms like: Quantum Neural Networks, Quantum Boltzmann Machine, Quantum Principal Component Analysis, Quantum k-means algorithm, Quantum k-medians algorithm, Quantum Bayesian Networks and Quantum Support Vector Machines.
The seven top machine learning projects to fight against antimicrobial resistance are explained. Antimicrobial resistance is one of the key reasons of human sufferings in modern hospitals. Preventing microbes from developing resistance to drugs has become as important issue for treating illnesses across the world. Artificial Intelligence, machine learning, genomics and multi-omics data integration are the fast-growing emerging technologies to counter antimicrobial resistance problems. Here, Dr. Amit Ray explains how these technologies can be used in seven key areas to counter antimicrobial resistance issues.
Five Key Benefits of Quantum Machine Learning
Here, Dr. Amit Ray discusses the five key benefits of quantum machine learning.
Quantum machine learning is evolving very fast and gaining enormous momentum due to its huge potential. Quantum machine learning is the key technology for future compassionate artificial intelligence. In our Compassionate AI Lab, we have conducted several experiments on quantum machine learning in the areas of drug-discovery, combating antibiotic resistance bacteria, and multi-omics data integration.
We have realized that in the area of drug design and multi-omics data integration, the power of deep learning is very much restricted in classical computer. Hence, with limited facilities, we have conducted many hybrid classical-quantum machine learning algorithm testing at our Compassionate AI Lab.
What is holding back the large scale implementation of machine learning systems in healthcare and precision medicine? In this article Dr. Amit Ray, explains the key obstacles and challenges of implementing large-scale machine learning systems in healthcare. Dr. Ray argued that lack of deeper integration, incomplete understanding of the underlying molecular processes of disease it is intended to treat, may limit the progress of implementing large-scale machine learning based reliable systems in healthcare. Here, nine obstacles of present day machine learning systems in healthcare are discussed.
Machine Learning in Healthcare
Recently, machine learning algorithms, especially deep learning has shown impressive performance in many areas of medical science, especially in classifying imaging data in different clinical domains. In academic environment, Deep learning and Reinforcement learning methods of Artificial Intelligence (AI) has shown tremendous success in numerous clinical areas such as: Omics data integration (such as genomics, proteomics or metabolomics), prediction of drug-disease correlation based on gene expression, and finding combinations of drugs that should not be taken together. Deep learning is very successful in predicting cancer outcome based on tumour tissue images. Machine learning are used for medical decision support systems for ICU and critical care. Artificial Intelligence in Healthcare Current Trends discusses the current status of AI in healthcare. Read More »What’s Holding Back Machine Learning in Healthcare
How many qubits are needed to out-perform conventional computers, how to protect a quantum computer from the effects of decoherence and how to design more than 1000 qubits fault-tolerant large scale quantum computers, these are the three basic questions we want to deal in this article. Qubit technologies, qubit quality, qubit count, qubit connectivity and qubit architectures are the five key areas of quantum computing are discussed.
Earlier we have discussed 7 Core Qubit Technologies for Quantum Computing, 7 Key Requirements for Quantum Computing. Spin-orbit Coupling Qubits for Quantum Computing and AI, Quantum Computing Algorithms for Artificial Intelligence, Quantum Computing and Artificial Intelligence , Quantum Computing with Many World Interpretation Scopes and Challenges and Quantum Computer with Superconductivity at Room Temperature. Here, we will focus on practical issues related to designing large-scale quantum computers.
Instead of running on zeros and ones, quantum computers run on an infinite number of states between zero and one. Instead of performing one calculation before moving on to the next, quantum computers can manage multiple processes all simultaneously.
Unlike binary bits of information in ordinary computers, “qubits” consist of quantum particles that have some probability of being in each of two states, represent as |0⟩ and |1⟩, simultaneously. When qubits interact, their possible states become interdependent (entangled), each one’s chances of |0⟩ and |1⟩ hinging on those of the other. Moreover, quantum information does not have to be encoded into binary bits, it could also be encoded into continuous observables bits (qubits).
The speed requirements for various applications grows with the complexity of the problems and the speed advantage of quantum computers are enormous compare to classical computers. The key to quantum computation speed is that every additional qubit doubles the potential computing power of a quantum machine.
The objective of 1000 qubits fault-tolerant quantum computing is to compute accurately even when gates have a high probability of error each time they are used. Theoretically, accurate quantum computing is possible with error probabilities above 3% per gate, which is significantly high. The resources required for quantum computing depends on the error probabilities of the gates. It is possible to implement non-trivial quantum computations at error probabilities as high as 1% per gate.Read More »Roadmap for 1000 Qubits Fault-tolerant Quantum Computers
Quantum Computing with Many World Interpretation Scopes and Challenges
Many scientist believe that Many World Interpretation (MWI) of quantum mechanics is self-evidently absurd for quantum computing. However, recently, there are many groups of scientist increasingly believing that MWI has the real future in quantum computing, because MWI can provide true quantum parallelism. Here, I briefly discuss the scopes and challenges of MWI for future quantum computing for exploration into the deeper aspects of qubits and quantum computing with MWI.
The heart of Copenhagen interpretation based quantum computing is wave function collapse. However, the Copenhagen interpretation fails to specify precisely where and how the collapses occurs and is therefore an imprecise theory. Moreover, the exact collapse mechanism is not scientifically well defined. But the beauty of Copenhagen interpretation is that “it works” and it makes no unnecessary assumptions. According to the Copenhagen interpretation there is no “game” only the result is real – the physical reality is a result of the collapse of the wave function as a “local” manifestation of the non-local wave-function. However, wave function collapse is widely regarded as artificial and adhoc.
On the other hand, MWI removed the wave function collapse theory and focused on quantum parallelism thesis (QPT). It states that none of the quantum states vanishes at all, except to our perception. It says, in essence, let’s just do away with wave function collapse altogether. The entire universe (all the universes together) is described by a gigantic wave function that contains within it all possible realities. This wave function is known as “universal wave function”. The universe is a single reality. It hypothesized that, at the quantum level, whenever the universe is confronted with a choice of paths, reality splits into branches and both choices or paths happen simultaneously.
This tutorial is for the researchers, volunteers and students of the Compassionate AI Lab for understanding the deeper aspects of quantum computing for implementing large-scale compassionate artificial intelligence projects.
The past present and future deep learning methods and approaches are reviewed. It covers both deep reinforcement learning and deep supervised and unsupervised learning techniques in both technical and historical perspective.