If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Falkonry releases its 2019 Predictive Operations Readiness Report based on the results of 300 organizations who used a calculator developed by the company to assess their machine learning and artificial intelligence (AI) readiness. The results provide a look at where companies see themselves from a maturity level and were remarkably consistent across the globe, according to Falkonry.
This paper deals with the use of Artificial Intelligence Methods (AI) in the design of new molecules possessing desired physical, chemical and biological properties. This is an important and difficult problem in the chemical, material and pharmaceutical industries. Traditional methods involve a laborious and expensive trial-and-error procedure, but computer-assisted approaches offer many advantages in the automation of molecular design.
I am working in ML/AI field for 6 years and apart from technical skills that I acquired while working on the projects, I have also discussed various aspects of ML/AI with my non-technical colleagues, who have mostly been senior manager, VPs or CXOs. When I heard about "AI For Everyone" course, I was a bit reluctant in attending it as I thought I know most of the generic stuff that might have been talked in the course. Recently, one of my colleagues discussed with me a few topics covered in this course which intrigued me to get a fresh perspective on these topics. So, I recently attended this course on Coursera. My motivation to write this blog is to make sure that I have understood key aspects of this course and am able to make my non-technical colleagues and project stakeholders understand the benefits & limitations of using AI.
The bigger your company, the more important it is that every team member is on the same page. When you're as big as Genpact, with 90,000 employees and twice as many partners, then collaboration is a top priority. Sanjay Srivastava is well aware of the challenges. As Genpact's Chief Digital Officer, he is front and center at the effort to make sure the disparate teams and employees within the company are working successfully in a collaborative organizational culture, as well as offering a satisfying customer experience. For Sanjay, there are three main factors that need a strong collaboration platform within a company. It starts with the idea of the business as a connected ecosystem that drives a collective intelligence. Then there's the concept of continuous learning and innovation that requires a collaborative framework to be successful. Finally, there's the convergence of domains, the ability to pull people together from different disciplines, with different experiences, and across ...
You are invited to attend our event next Monday, Nov 18th @6:00 pm at Venture X. Come and join us as Dr. Huang gives a talk on how Deep Learning is used in Genomics. If you are curious about Artificial Intelligence & Data Science in Genomics and want to learn more, then this talk is for you. Dr. Huang's expertise is in the areas of Computational Biology, Computational Neuroergonomics, Brain-Computer Interface, Statistical Modeling, and Bayesian Methods. Dr. Yufei Huang is a Professor and Associate Chair in Research at the Department of Electrical and Computer Engineering at UTSA. He is also an adjunct professor at the Dept. of Epidemiology and Biostatistics at the University of Texas Health Science Center at San Antonio.
Microsoft's Ignite event traditionally attracts more from the developer ranks, but the technologies on display are increasingly of relevance to CIOs developing cloud strategies today. At Ignite 2019 in Orlando last week, Microsoft unveiled a new approach to analytics and data warehousing, Azure Synapse Analytics, and a new way to run Azure data services in anyone's cloud, Azure Arc. Get the latest cloud computing insights by signing up for our newsletter. With Azure Synapse Analytics Microsoft takes its Azure SQL Data Warehouse and turns up the volume to handle petabytes of data in its cloud. Some of the features -- such as dynamic data masking and column- and row-level security to provide granular access control -- are already generally available, while others -- notably integrations with Apache Spark, Power BI and Azure Machine Learning -- are still in preview.
Machine learning (ML) methods reach ever deeper into quantum chemistry and materials simulation, delivering predictive models of interatomic potential energy surfaces1,2,3,4,5,6, molecular forces7,8, electron densities9, density functionals10, and molecular response properties such as polarisabilities11, and infrared spectra12. Large data sets of molecular properties calculated from quantum chemistry or measured from experiment are equally being used to construct predictive models to explore the vast chemical compound space13,14,15,16,17 to find new sustainable catalyst materials18, and to design new synthetic pathways19. Recent research has explored the potential role of machine learning in constructing approximate quantum chemical methods20, as well as predicting MP2 and coupled cluster energies from Hartree–Fock orbitals21,22. There have also been approaches that use neural networks as a basis representation of the wavefunction23,24,25. Most existing ML models have in common that they learn from quantum chemistry to describe molecular properties as scalar, vector, or tensor fields26,27.
The impact of artificial intelligence in the healthcare sector is undeniably potent, as we have seen and explored over the last few months, AI has the potential to truly revolutionize and every factor of modern healthcare. From diagnostic purposes such as we see at Moorfield's NHS Trust dealing with complex optical coherence tomography scans, or OCT scans for short, to the systems built by giants such as Philips Healthcare and Cerner who manage the daily management of the hospital. However, most clinicians agree that artificial intelligence has an assistive role to play in healthcare, far from a leading role, and as such these systems will help clinicians to assign priorities to patients, spot the easily missed features in specimens of medical imaging and generally speed up the process of a patient's journey through healthcare; although it must be mentioned that AI does what humans can do faster and more accurately in terms of assessing X-rays, MRIs and so forth, as you will have seen vividly boldly titled amongst most major technology news platforms, but these claims are in need of long term assessment with far more diversified patient sets that would be more typical of the overcrowded NHS wards that bring people from all walks of life for the common goal of an unconditionally excellent healthcare. As, with consent, data sets grow through the hospital's daily running as doctors request medical imaging modalities throughout the day, constantly improving the apt of the systems in place; as the data grows, so does the performance of the system.