Knowledge graphs (KGs) generated by large language models (LLMs) are becoming increasingly valuable for Retrieval-Augmented Generation (RAG) applications that require knowledge-intensive reasoning. However, existing KG extraction methods predominantly rely on prompt-based approaches, which are inefficient for processing large-scale corpora. These approaches often suffer from information loss, particularly with long documents, due to the lack of specialized design for KG construction. Additionally, there is a gap in evaluation datasets and methodologies for ontology-free KG construction. To overcome these limitations, we propose SynthKG, a multi-step, document-level ontology-free KG synthesis workflow based on LLMs. By fine-tuning a smaller LLM on the synthesized document-KG pairs, we streamline the multi-step process into a single-step KG generation approach called Distill-SynthKG, substantially reducing the number of LLM inference calls. Furthermore, we re-purpose existing question-answering datasets to establish KG evaluation datasets and introduce new evaluation metrics. Using KGs produced by Distill-SynthKG, we also design a novel graph-based retrieval framework for RAG. Experimental results demonstrate that Distill-SynthKG not only surpasses all baseline models in KG quality -- including models up to eight times larger -- but also consistently excels in retrieval and question-answering tasks. Our proposed graph retrieval framework also outperforms all KG-retrieval methods across multiple benchmark datasets. We release the SynthKG dataset and Distill-SynthKG model publicly to support further research and development.

At the US Patent & Trademark Office in Alexandria, Virginia, artificial intelligence (AI) projects are expediting the patent classification process, helping detect fraud, and expanding examiners' searches for similar patents, enabling them to search through more documents in the same amount of time. And every project started with a pilot project. "Proofs of concept (PoCs) are a key approach we use to learn about new technologies, test business value assumptions, de-risk scale project delivery, and inform full production implementation decisions," says USPTO CIO Jamie Holcombe. Once the pilot proves out, he says, the next step is to determine if it can scale. Indian e-commerce vendor Flipkart has followed a similar process before deploying projects that allow for text and visual search through millions of items for customers who speak 11 different languages.

The disparity between digital transformation leaders and laggards stems from a complex web of overlapping factors -- which often speak more to organizational issues than technical difficulties. Considerations in play include corporate history, IT philosophy, the ability to deliver on customer experience and a product vs. project mindset. A particularly important element separating a successful digital business from its competitors is a knack for translating small successes into enterprise-wide benefits. Indeed, overcoming digital transformation challenges at scale is crucial for realizing the promise of technology-infused business models, according to CIOs and industry analysts. Companies playing catch-up in the digital race must first focus on the essentials, such as customer experience, before moving on to more innovative pursuits.

Prof. Yanay Ofran's amazing story about the pursuit of an antibody that will save the world from disease Shlomit Lan and Gali Weinreb Professor Yanay Ofran, founder and CEO of Biolojic Design, a company that develops smart antibodies designed to treat a variety of diseases, is frustrated. "Humanity invests $300 billion each year in drug development, and what do we get? At most, we get a few dozen medications a year, most of which don't solve the problems, and give an additional three weeks of life on average to patients with pancreatic cancer, or manage to inject a medication that to date was given via infusion. Those are the breakthroughs," he says despairingly. But Ofran does not think the pharmaceutical companies are the only culprit. "The drug companies are portrayed as a devil who says, 'I won't cure this because it's not worth my while.' But these companies do have a legal obligation towards their shareholders, not to develop drugs unless there's an economic incentive. The problem, as analyzed by Ofran, is much more complicated and therefore far more difficult to treat. "There are three players sitting around the drug development table: science, regulation and the business world.

INDIANAPOLIS--When Covid-19 struck, drug companies around the world began racing to find vaccines and treatments. One factor has gummed up their efforts: They have to work in an environment transformed by the very problem they are trying to tackle. At Eli Lilly & Co., the chief of a laboratory, quarantining at home after he contracted Covid-19, had to use a robot equipped with an iPad to patrol his lab. When shipping constraints threatened to delay testing of an experimental drug, Lilly repurposed its corporate jet to get vials...

The drug, baricitinib, is currently marketed by Eli Lilly to treat rheumatoid arthritis. Now, thanks to AI, it is being tested against COVID-19 in a major randomised-controlled trial in collaboration with the U.S. National Institute for Allergies and Infectious Diseases (NIAID) in combination with remdesivir, an antiviral drug from Gilead Sciences that recently won emergency-use approval for COVID-19. Eli Lilly has now commenced its own independent trial of baricitinib as a therapy for COVID-19 in South America, Europe and Asia.

As the COVID-19 pandemic continues to infect people across the world, a technological application already familiar to many in the biotech field is lending a key supporting role in the fight to treat and stop it: artificial intelligence (AI). AI is currently being used by many companies to identify and screen existing drugs that could be repurposed to treat COVID-19, aid clinical trials, sift through trial data, and scour through patient electronic medical records (EMRs). The power of AI in COVID-19 is that it is being used to generate actionable information--some of which would be impossible without AI--much more quickly than before. A simple definition of AI is the ability of a computer to rapidly think and learn. AI utilizes machine learning to analyze large amounts of data.

Variational AI Inc.'s bread and butter rests in novel drug discovery, specifically using artificial intelligence (AI) to compress the years-long preclinical process to perhaps a single year. But in the midst of a pandemic, even a year might be too long to find a treatment for COVID-19, according to CEO Handol Kim. "Even if we're able to collapse the front end, you still have five or six years of clinical trials and who knows if we need a drug in five or six years for COVID-19?" he said. "We thought, 'Well, the fastest way to do this is repurposing existing drugs.'" The pitch caught the interest of the Digital Technology Supercluster, which last month committed to spending $60 million of its $153 million budget to develop partnerships across its networks to address issues brought on by the pandemic.