When companies first start deploying artificial intelligence and building machine learning projects, the focus tends to be on theory. Is there a model that can provide the necessary results? How can it be built? How can it be trained? But the tools that data scientists use to create these proofs of concept often don't translate well into production systems.

This blog post is the continuation of my previous articles part 1, part 2 and part 3. Caution: The Difference Between Training Metrics and Evaluation Metrics Sometimes, the model training procedure uses a different metric (also known as a loss function) than the evaluation. This can happen in the instance when we are re-appropriating a model for a different task than it was designed for. For example, we might train a personalized recommender by minimizing the loss between its predictions and observed ratings, and then use this recommender to produce a ranked list of recommendations. This is not an optimal scenario. It makes the life of the model difficult by asking it to do a task that it was not trained to do.

A century ago, English mathematician Lewis Fry Richardson proposed a startling idea for that time: constructing a systematic process based on math for predicting the weather. In his 1922 book, "Weather Prediction By Numerical Process," Richardson tried to write an equation that he could use to solve the dynamics of the atmosphere based on hand calculations. It didn't work because not enough was known about the science of the atmosphere at that time. "Perhaps some day in the dim future it will be possible to advance the computations faster than the weather advances and at a cost less than the saving to mankind due to the information gained. But that is a dream," Richardson concluded.

In a time when the pace of change is accelerating, the presence of creative excellence for businesses is crucial for success. However, it is easier said than done. Creative excellence with humans alone has its setbacks, preventing it from reaching its full potential. That's where artificial intelligence comes in. AI is an extraordinary force for creative excellence.

Digital pathology is an emerging field which deals with mainly microscopy images that are derived from patient biopsies. Because of the high resolution, most of these whole slide images (WSI) have a large size, typically exceeding a gigabyte (Gb). Therefore, typical image analysis methods cannot efficiently handle them. Seeing a need, researchers from Boston University School of Medicine (BUSM) have developed a novel artificial intelligence (AI) algorithm based on a framework called representation learning to classify lung cancer subtype based on lung tissue images from resected tumors. We are developing novel AI-based methods that can bring efficiency to assessing digital pathology data.

May 11 (Reuters) - The science-fiction is harder to see in Google's second try at glasses with a built-in computer. A decade after the debut of Google Glass, a nubby, sci-fi-looking pair of specs that filmed what wearers saw but raised concerns about privacy and received low marks for design, the Alphabet Inc (GOOGL.O) unit on Wednesday previewed a yet-unnamed pair of standard-looking glasses that display translations of conversations in real time and showed no hint of a camera. The new augmented-reality pair of glasses was just one of several longer-term products Google unveiled at its annual Google I/O developer conference aimed at bridging the real world and the company's digital universe of search, Maps and other services using the latest advances in artificial intelligence. "What we're working on is technology that enables us to break down language barriers, taking years of research in Google Translate and bringing that to glasses," said Eddie Chung, a director of product management at Google, calling the capability "subtitles for the world." Selling more hardware could help Google increase profit by keeping users in its network of technology, where it does not have to split ad sales with device makers such as Apple Inc (AAPL.O)and Samsung Electronics CO (005930.KS)that help distribute its services.

Using artificial intelligence (AI) to enhance teaching and learning has been a kind of nirvana for education leaders for several years now – a place of perhaps unimagined power that has perpetually seemed just out of grasp. And though it may feel as if it's always just around the next corner, forever one tool or dataset away, one entrepreneur says we're getting closer. In fact, he says we may be close enough to say we've actually arrived at the place where AI products and systems are already showing the return they've promised for so long – personalizing learning for students, yielding rich and actionable data, simplifying teaching practices and, best of all, improving learning outcomes. That positive assessment comes from Ramesh Balan, the founder and CEO of Knomadix – the buzz-worthy AI education company he launched in 2015. And Balan may be worth listening to.

Artificial intelligence (AI) algorithms trained on real astronomical observations now outperform astronomers in sifting through massive amounts of data. AI helps them to find new exploding stars, identify new types of galaxies and detect the mergers of massive stars, accelerating the rate of new discovery in the world's oldest science. But AI, also called machine learning, can reveal something deeper, University of California, Berkeley, astronomers found: unsuspected connections hidden in the complex mathematics arising from general relativity -- in particular, how that theory is applied to finding new planets around other stars. In a paper appearing this week in the journal Nature Astronomy, the researchers describe how an AI algorithm developed to more quickly detect exoplanets when such planetary systems pass in front of a background star and briefly brighten it -- a process called gravitational microlensing -- revealed that the decades-old theories now used to explain these observations are woefully incomplete. In 1936, Albert Einstein himself used his new theory of general relativity to show how the light from a distant star can be bent by the gravity of a foreground star, not only brightening it as seen from Earth, but often splitting it into several points of light or distorting it into a ring, now called an Einstein ring.

May 26 (Reuters) - China's search engine giant Baidu Inc surpassed quarterly revenue estimates on Thursday as a resurgence of COVID-19 in China and accompanying restrictions boosted demand for its cloud and artificial intelligence (AI) products. The news drove Baidu's U.S.-listed shares more than 5% up in pre-market trading even as the company cautioned that the second quarter would be more challenging. Revenue for the three months to March 31 rose 1% to 28.41 billion yuan ($4.22 billion), the slowest growth in six quarters, but topped an analysts' average estimate of 27.82 billion, IBES data from Refinitiv showed. It posted a net loss of 885 million yuan, or 2.87 yuan per American Depository Share (ADS), amid an economic downturn and pandemic resurgence in China. A year earlier it had posted a profit of 25.65 billion yuan, or 73.76 yuan per ADS.

Since then, "Diablo Immortal" has boosted its reputation, as alpha and beta tests proved the game was a full-throated, classic Diablo experience. The Diablo series is one of the most influential in modern game design, popularizing gameplay loops that center acquiring randomized "loot" to make your role-playing character more powerful. "Diablo 2," which was recently remastered, cemented this loop, while "Diablo 3," which Cheng also worked on, streamlined and evolved it.