This study explores the potential of natural language models, including large language models, to extract causal relations from medical texts, specifically from Clinical Practice Guidelines (CPGs). The outcomes causality extraction from Clinical Practice Guidelines for gestational diabetes are presented, marking a first in the field. We report on a set of experiments using variants of BERT (BioBERT, DistilBERT, and BERT) and using Large Language Models (LLMs), namely GPT-4 and LLAMA2. Our experiments show that BioBERT performed better than other models, including the Large Language Models, with an average F1-score of 0.72. GPT-4 and LLAMA2 results show similar performance but less consistency. We also release the code and an annotated a corpus of causal statements within the Clinical Practice Guidelines for gestational diabetes.

Embedding undirected graphs in a Euclidean space has many computational benefits. FastMap is an efficient embedding algorithm that facilitates a geometric interpretation of problems posed on undirected graphs. However, Euclidean distances are inherently symmetric and, thus, Euclidean embeddings cannot be used for directed graphs. In this paper, we present FastMap-D, an efficient generalization of FastMap to directed graphs. FastMap-D embeds vertices using a potential field to capture the asymmetry between the to-and-fro pairwise distances in directed graphs. FastMap-D learns a potential function to define the potential field using a machine learning module.

The artificial intelligence (AI) renaissance is largely due to advances in deep learning, a type of machine learning with architectural elements inspired by the biological brain. However, unlike the energy-efficient human brain, the process of training large scale deep neural networks is enormously energy-intensive, requiring colossal amounts of computing memory and power. In a world's first, IBM Research reveals at this week's NeurIPS conference an unprecedented 4-bit AI training system that may help reduce machine learning's heavy carbon footprint. "Training AI models has become extremely expensive and generates a massive carbon footprint. IBM Research over the last five years has introduced a number of key techniques to address these challenges and dramatically improve how we train neural network models," said Kailash Gopalakrishnan, IBM Fellow and Senior Manager, Accelerator Architectures and Machine Learning, IBM Research.

BENGALURU: As India plans to roll out a nationwide facial recognition system this year, Infosys co-founder Kris Gopalakrishnan believes that the country must develop its own databases for efficient implementation of breakthrough technologies that use artificial intelligence and machine learning. A facial recognition system is a technology capable of identifying or verifying a person by analysing patterns based on the person's facial textures and shape. Gopalakrishnan noted that India should carry out its own trials before implementing the facial recognition systems, as currently the algorithms used to train these mostly employ data of white men belonging to the Anglo-Saxon community, and it is unclear whether it will work properly in the country. "We also need to look at biases. One of the reasons why I believe India must do research in artificial intelligence (AI) and machine learning (ML) particularly is because most of the databases that are used to train these systems which we use today are being trained with data which is not from India," he told PTI in an interview on the sidelines of the Infosys Prize ceremony here.

Innovative start-ups can play a major role in the Indian healthcare system as the fourth industrial revolution is characterised by a fusion of technologies that is blurring the lines between the physical, digital and biological domains, according to Kris Gopalakrishnan, Chairman, Axilor Ventures Private Ltd, Bengaluru. Delivering the 27th convocation address of Manipal Academy of Higher Education (MAHE) in Manipal on Friday, Gopalakrishnan, said that there is tremendous disruption at the edge and at the intersection of emerging technology domains and economic activity. Stating that Artificial Intelligence (AI) is becoming one of the most important technologies of all time, he said AI is now getting deeper into what were so far specialist human domains. Referring to the example of a real-time image-guided and robot-assisted surgery where imaging coupled with robotic assistance helps in assessing the area of procedure, monitoring the tools in 3D, and updating patho-physiology knowledge of the targeted tissue in real-time, Gopalakrishnan said this innovation is at the intersection of AI, robotics, biotechnology, telecommunications and clinical domains. Many more such innovations are emerging and defining the 21st Century, he said.

With new technologies disrupting businesses and changing the rules of engagement, India faces a daunting task to reskill its huge workforce for Artificial Intelligence (AI), Infosys co-founder Kris Gopalakrishnan says. "India has a major challenge of transitioning its young workforce to the fourth industrial revolution called AI after the eras of agriculture, manufacturing and services," Gopalakrishnan said in an interview. Gopalakrishnan, 63, well-known as'Kris', is one of the seven co-founders of the iconic IT firm, who became its chief executive after fellow co-founder Nandan Nilekani quit in mid-2009 to set up the Unique Identification Authority of India (UIDAI) for issuing Aadhaar cards to over a billion citizens. "As the large workforce is engaged in diverse occupations such as agriculture, manufacturing and white-collar jobs in the services sector, it needs to be re-skilled to sustain the jobs, as AI will replace traditional jobs," said Gopalakrishnan. Originating in the mid-1950s as an academic discipline, AI involves machines emulating human intelligence.

The field of deep learning is still in flux, but some things have started to settle out. In particular, experts recognize that neural nets can get a lot of computation done with little energy if a chip approximates an answer using low-precision math. But some tasks, especially training a neural net to do something, still need precision. IBM recently revealed its newest solution, still a prototype, at the IEEE VLSI Symposia: a chip that does both equally well. The disconnect between the needs of training a neural net and having that net execute its function, called inference, has been one of the big challenges for those designing chips that accelerate AI functions.

At a time when technologies like Artificial Intelligence are becoming the new world order, Karnataka is betting big to prepare itself for these new drivers of employment. Drones that monitor crop health, medical devices for early detection of cancer and apps that help visually impaired read and identify objects were some of the AI--based innovations on display at the Bengaluru Tech Summit 2017. Many of these companies pitched their products and services to an audience of top business executives, government officials, and investors at Karnataka government's flagship event held in Palace Grounds here. "We are at the beginning of what is called as fourth industrial revolution," said Kris Gopalakrishnan, co-founder of software giant Infosys. He said multinational companies are setting up research and development facilities here because they are able to find professionals at a scale who understand technologies such as AI and Machine Learning.

With Elon Musk and Mark Zuckerberg sparring over its ethics and China announcing its intention to create a $150 billion domestic industry based on it, Artificial Intelligence is perhaps the most discussed topic in the tech news cycle. It's likely to be a talking point no matter what your favourite watering hole for tech news. Billions of dollars have been invested by VCs in AI since 2016 with the US and China leading the race in record funding in terms of deals and dollars. In sharp contrast, Indian startups have collectively raised less than $100 million from (2014-2017YTD), according to data from startup analytics firm Tracxn -- that's smaller than Andrew Ng's recently launched $150 million VC fund. Another way to look at it: Grammarly, a Valley-based spell check tool raised more dollars than all of India's AI startups put together in the past three and a half years.