Microsoft – From Rudderless Giant to AI FirstMicrosoft is one of the world's most ubiquitous technology company. Best known for their Microsoft Windows operating systems the company also produces office suites, games, Azure, Xbox and many more. However, Microsoft is also at the forefront of developing artificial intelligence technology. As well as the potentially revolutionary Project Hanover, Microsoft AI applications are transforming businesses, services and how we interact with the world. They are also making major purchases and investments, such as acquiring LinkedIn or software development hosts GitHub. However, this has not always been the case. Microsoft was initially slow to realise the potential of AI. In the course of this article, we will explore how Microsoft, despite being slow off the mark, has transformed themselves into a leading AI-first company. We will also highlight some of Microsoft AIs most exciting innovations. By exploring some of Microsoft AIs leading initiatives we will see how then have been able to evolve into a leading AI company. Microsoft was formed in Albuquerque, New Mexico on the 4th of April 1975 by Paul Allen and Bill Gates. The company's name, Microsoft is a portmanteau of the words microcomputer and software. Success was swift, as was early growth. At the end of 1979, the company opened its first international office in Japan. Following a move to Washington in 1979, the company restructured in 1981 becoming an incorporated business.

As part of Microsoft's Project Hanover, biomedical researchers from the Jackson Laboratory are refining an artificial intelligence tool that "reads" medical documents to inform the development of precision cancer treatments, per an Oct. 27 Microsoft blog post. The Bar Harbor, Maine-based Jackson Laboratory developed a searchable database of complex genomic information that can be sorted and interpreted to improve outcomes and share information about clinical trials and treatments. To speed this process, the lab's researchers are applying Microsoft's machine reading AI, which automatically extracts from thousands of medical and research documents only the most relevant information about cancer mutations, drugs and patient responses. The partnership is mutually beneficial: Microsoft's AI tool is increasing the lab team's efficiency in curating their Clinical Knowledgebase, while their usage is simultaneously validating the AI's accuracy and effectiveness in "reading" documents. "Our goal is to make the human curators superpowered," said Hoifung Poon, Project Hanover's lead researcher and director of precision health natural language processing with Microsoft's research organization.

Microsoft this week announced new progress in its genomics collaboration with the Bar Harbor, Maine-based Jackson Laboratory. WHY IT MATTERS Jackson Lab has been using artificial intelligence tools, developed as part of Microsoft's Project Hanover, to help manage the vast amount of genomic data needed to power its precision medicine initiatives. Specifically, the technology has helped the laboratory scale up its Clinical Knowledgebase, or CKB too – a vast searchable database that helps oncologists and other healthcare experts make detailed interpretations of complex sequencing and maintain troves of leading-edge insights – drawn from thousands of cancer research papers each day – to help drive personalized treatments. The machine learning technology, which is still evolving, is increasingly able to "read" complex medical and research documents – trained to highlight important and relevant information contained within them such as new insights into genetics, drugs and patient response. That mining of disparate knowledge sources means clinicians can save hours finding and curating relevant data, targeted to specific genomic profiles.

Biomedical researchers are embracing artificial intelligence to accelerate the implementation of cancer treatments that target patients' specific genomic profiles, a type of precision medicine that in some cases is more effective than traditional chemotherapy and has fewer side effects. The potential for this new era of cancer treatment stems from advances in genome sequencing technology that enables researchers to more efficiently discover the specific genomic mutations that drive cancer, and an explosion of research on the development of new drugs that target those mutations. To harness this potential, researchers at The Jackson Laboratory, an independent, nonprofit biomedical research institution also known as JAX and headquartered in Bar Harbor, Maine, developed a tool to help the global medical and scientific communities stay on top of the continuously growing volume of data generated by advances in genomic research. The tool, called the Clinical Knowledgebase, or CKB, is a searchable database where subject matter experts store, sort and interpret complex genomic data to improve patient outcomes and share information about clinical trials and treatment options. The challenge is to find the most relevant cancer-related information from the 4,000 or so biomedical research papers published each day, according to Susan Mockus, the associate director of clinical genomic market development with JAX's genomic medicine institute in Farmington, Connecticut.

Microsoft researchers are doing a bug bash on cancer, complete with software code names like "Project Hanover." Some of them are actually drilling down into our genetic code, looking for ways to reprogram the immune system to combat cancer cells more effectively. "If you can do computing with biological systems, then you can transfer what we've learned in traditional computing into medical or biotechnology applications," Microsoft's Neil Dalchau says in the company's in-depth report about its cancer moonshots. Others are enlisting the power of cloud computing to identify which treatment would work best for a particular cancer patient, based on his or her personalized medical profile. Microsoft and AstraZeneca are already using a software tool known as the Bio Model Analyzer to figure out why leukemia patients respond differently to different treatments.