How to make an accurate prediction of climate change and greenhouse carbon emissions with artificial intelligence? We are experimenting with several AI models for climate change and global warming. Sri Amit Ray explains the hybrid system of process-based earth system models and the deep learning networks of artificial intelligence. 

As climate change and global warming have become the most urgent issues for human survival, researching ways to improve climate change and earth system models has become of the utmost importance.

Artificial intelligence (AI), specifically deep learning algorithms, has the ability to make decisive interpretations of large amounts of complex data. Moreover, modern machine learning techniques appear as the most effective tool for the analysis and understanding of ocean, earth, ice formations, CO2 emissions, biodiversity, and atmospheric data for situation and target specific dynamic prediction. One of the strengths of machine learning tools such as convolutional neural network is its capacity to combine a variety of methods.

Deep Transfer Learning Models for Biodiversity, Climate Change, and Global Warming

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Key Artificial Intelligence Projects to Fight Against COVID-19

Dr. Amit Ray 
Compassionate  AI Lab 

Prevention and early healing are the primary requirements for the present COVID crisis. 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.

AI to Fight COVID 19 Researches Sri Amit Ray Compassionate AI Lab

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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. 

Balance control in elderly people is one of the key issues of old age. Artificial Intelligence can play a big role to solve this issue. In this research work, we demonstrate the application of machine learning techniques for posture alignments and the control of the body center of mass for disabled people.

Designing automated balance control system for elderly people is one of the key projects of our Compassionate AI Lab. Here, Dr. Amit Ray discuses about one of the recent projects of AI using deep learning algorithms for automatic balance control of elderly people. He explains how machine learning algorithms can be used to study and improve the dynamical properties of postural stability of elderly people. The project focuses on how image recognition, human-body joint dynamics, and path navigation methods of artificial intelligence can be used  to eliminate the imbalance, fall and injury of elderly people or for physically challenged people.

Compassionate Artificial Intelligence can be used for helping elderly people in many ways. Here, we discuss about one of our recent project of using AI & deep learning techniques for automatic balance control. The machine learning algorithms are used to improve dynamical properties of postural stability. In this project AI based machine learning algorithms are used to find the insights into the person specific postural strategies for older adults in order to adapt to the postural challenges during sleeping, standing, turning and walking. To study the body movement behavior of elderly people accurately, it is necessary to observe and record their movement trajectory and joint movements quantitatively and precisely in three dimensions.

Artificial Intelligence to Combat Antibiotic Resistant Bacteria

Artificial Intelligence to Combat Antibiotic Resistant Bacteria - tools, techniques, models, scopes and challenges are discussed. Antibiotic resistance bacteria are one of the key research area of our Compassionate AI Lab. Dr. Amit Ray explains how artificial intelligence can be used in combating these superbugs. Antibiotic resistance bacteria is becoming world’s biggest health crisis. We discussed here multi-agent deep reinforcement learning models for predicting behavior of bacteria and phages in multi-drug environments.  We call this model as DeepCombat.

Antibiotic resistant bacteria are bacteria that are not controlled or killed by antibiotics. They are able to survive and even multiply in the presence of an antibiotic.  These bacteria currently kill an estimated 700,000 people globally each year – a death toll which could rise to 10 million a year by 2050 if we don’t act [1]. The main difficulty is that the bacteria are changing fast. They changing faster than we can change the drugs in response.

 Artificial intelligence is showing alternative means of fighting these deadly infections and killer bacteria. Multi-drug-resistant bacterial infections annually result in millions of hospital days, billions in healthcare costs, and, most importantly, thousands of lives lost. Artificial Intelligence for healthcare is progressing at an exponential rate.  We are evaluating here, the role of artificial intelligence in fighting these superbugs.  Especially, the use of AI for intelligent Phage therapy.

Do you know according to WHO, there are about 39 million people in the world who are blind? Artificial Intelligence is one of our key research area to overcome that challenge. Here, we explain the use of AI based grid cell, place cell and path integration strategies to solve the problems.

Dr. Amit Ray explains how grid cell, place cell and path integration strategies with artificial intelligence can be used  for designing the navigation system for blind people. Here, we discuss the use of AI techniques for automatic navigation.

Brain-Computer Interface and Compassionate Artificial Intelligence

Dr. Amit Ray

The purpose of Compassionate AI is to remove the pain from the society and help humanity. We focused on developing AI based low cost BCI based interfaces for helping disable people.

Artificial Intelligence with Brain-Computer Interface (BCI) or Brain Machine Interface (BMI) is a fast-growing emerging technology for removing pains from the society. Here,  Dr. Amit Ray explains how with the advancement of artificial intelligence and exploration of new mobile bio-monitoring  devices, earphones, neuroprosthetic, wireless  wearable sensors, it is possible to monitor  thoughts and activities of brain neurons  and serve humanity.

This research is going to be immensely  beneficial for the physically and mentally challenged people as well as for the people who are suffering from post-traumatic stress disorder (PTSD), and other mental disorders or brain problems. Over the last 5 years, technologies for non-invasive transmission of information from brains to computers have developed considerably.

Here, researchers focus to build a direct communication link between the human brain and the smartphones, earphone, computers or other devices. With BCI mind can speak silently with a smartphone or other devices.  Recent advancement of neuroprosthetic, linking the human nervous system to computers and providing unprecedented control of artificial limbs and restoring lost sensory function.

 BCI establishes two way communications between the brain and the machine.  One is brain-computer interface, and another is called computer-brain interfaces (CBI). BCI hopes to create new communication channels for disabled or elderly persons using their brain signals.

GPUs and Deep Learning for Compassionate Artificial Intelligence

Dr. Amit Ray discusses how GPUs and deep learning can be used for developing complex modules of the compassionate artificial intelligence. GPUs and cloud TPUs made complex deep learning of artificial intelligence possible in laptop, PC and smartphone. They made complex deep learning possible for research labs and smaller companies across the world. They provided a lot more computing power and efficiency for complex matrix operations and parallel processing. 

One of AI's biggest potential benefits is to keep humanity healthy, happy and free from inequalities and slavery. The role of AI is gradually changing. It is shifting from typical object recognition or diagnosis tool, to complex human like powerful integrated compassionate care giving systems. Compassionate AI is one area where AI is beginning to take strong hold. Here, Dr. Ray explains the implementation of deep learning modules of integrated compassionate care giving systems with GPUs and TPUs . 

Training of complex compassionate artificial intelligence modules requires deep learning neural networks. Training of compassionate modules are more time‐consuming compared to shallow learning models of AI. They may take months, weeks, days or hours depending on the size of training set and model architecture. This is because the number of computational steps increases rapidly with the number of elements in the matrix. In every doubling of the matrix size increases the length of the calculation eight-fold. Training on GPUs can considerably reduce this training time. It can reduce the time to tenfold or more. Hence,  the use of GPUs are also crucial for evaluating multiple deep learning models efficiently.

How GPUs Help Deep Learning

The core of many machine learning applications is the repeated computation of a complex mathematical expression. Neural networks store weights in matrices. Deep learning is mostly comprised of matrix operations like matrix multiplication. Emotional Intelligence modules of compassionate AI requires matrix operations of feeling like; anger, fear, depression, pain and pleasure. This is what people want a machine to exhibit for them to be trusted.  Linear algebra makes matrix operations  of feelings and emotions fast and easy, especially when training on Graphical Processing Units (GPUs). GPUs were developed to handle lots of parallel computations using thousands of cores. Instead of processing pixels one-by-one, GPUs manipulate entire matrices of pixels in parallel.  GPUs have almost 200 times more processors per chip than a CPU. CPU optimized serial tasks where as GPU optimized parallel tasks. For complex problems training of a deep learning networks takes months and days but with the help of GPU training will take hours or minutes. If you want to learn deep learning models quickly GPU is must. 

Deep learning is an emerging computer science field that seeks to mimic the human brain with hardware and software. The use of generalized  algorithm was crucial to the development of . The popularity of graphics cards for neural network training exploded after the advent of general purpose GPUs. Initially, GPUs were developed mainly for processing arrays of pixels for 3D video games.  The large memory bandwidth with parallel processing power, makes them ideal for implementing deep learning in compassionate AI.

Modules  and Algorithms of Compassionate AI

The core of developing compassionate AI algorithms is learning, growing, adding values and embracing challenges. They deals with possibilities, creativity, innovation, awe, and deep service to the humanity. The models like emotions, beliefs, values, imagination, long term memory, protective habits, critical thinking,  and caring man-machine behavior needs huge data processing and training.  Large parallel processing power and memory bandwidth of GPUs and quantum computing  are ideal for that.

Presently, machine learning tools integrated with the advancements in humanoid design are enabling machines to have compassionate conversations with human and having social interactions with people to keep aging minds sharp and happy. More details about the modules and the algorithms are explained in the book Compassionate Artificial Superintelligence AI 5.0.  

Modern Trends in GPUs

The growth of GPU is exponential. In 2007, NVIDIA launched the CUDA programming platform, and opened up the general purpose parallel processing capabilities of the GPU.  The CUDA toolkit is deeply entrenched. Now, it works with all major deep learning frameworks  like Tensoflow, Pytorch, Caffe, CNTK, etc. Deep learning with NVIDIA-CUDA is quite efficient. 

Google’s new cloud TPUv2 are powerful enough. They are designed for machine learning and tailored for TensorFlow. They are free TPU cluster for researchers, and a lightweight TensorFlow version for mobile devices. TPUs are provided as network addressable devices.

The most important feature for deep learning performance is memory bandwidth. GPUs are optimized for memory bandwidth. Memory bandwidth is the rate at which data can be read from or stored into a semiconductor memory by a processor. Operations like like matrix multiplication requires high memory bandwidth. 

Summary: 

Training of deep learning neural networks for compassionate artificial intelligence requires large numbers of matrix multiplications, which can be parallelized efficiently on GPUs. All state‐of‐the‐art deep learning frameworks provide support to train models on either CPUs or GPUs without requiring any knowledge about GPU programming. On desktop machines, the local GPU card can often be used if the framework supports the specific brand. Alternatively, commercial providers provide GPU and TPU cloud compute clusters are the easy way of implementing complex deep learning models of compassionate AI. 

References:

1. Compassionate Artificial Superintelligence AI 5.0, Amit Ray, 2018
2. Large-scale Deep Unsupervised Learning using Graphics Processors, Rajat Raina et al., 2009
3. Deep Learning,  Yann LeCun et al., Nature, 2015