If you ask pet owners to pick the most difficult part of their workday routine, many would say "mornings." They don't mean the common struggles with getting out of bed or commuting, but rather the emotions involved in leaving their beloved pets behind. The sad look in the pets' eyes suggests this is not an easy time for them either. Surveys conducted by ZenCrate and Zulily show that more than 32 million dogs in the US suffer anxiety when left alone at home, while 84 percent of pet parents frequently worry about their housebound fur babies. There are a number of solutions available, such as pet sitters or daycare facilities, but these do not allow people and pets to actually stay connected.

In recent years, machine learning has been very successful in solving a wide range of problems. In particular, neural networks have reached human, and sometimes super-human, levels of ability in tasks such as language translation, object recognition, game playing, and even driving cars. Prevent out-of-control infrastructure and remove blockers to deployments. With this growth in capability has come a growth in complexity. Data scientists and machine learning engineers must perform feature engineering, design model architectures, and optimize hyperparameters. Since the purpose of the machine learning is to automate a task normally done by humans, naturally the next step is to automate the tasks of data scientists and engineers. This area of research is called automated machine learning, or AutoML. There have been many exciting developments in AutoML recently, and it's important to take a look at the current state of the art and learn about what's happening now and what's coming up in the future. InfoQ reached out to the following subject matter experts in the industry to discuss the current state and future trends in AutoML space. InfoQ: What is AutoML and why is it important? Francesca Lazzeri: AutoML is the process of automating the time consuming, iterative tasks of machine learning model development, including model selection and hyperparameter tuning.

"Our destiny is to become what we think, to have our thoughts become our bodies and our bodies become our thoughts." The next major technological platform for creative expansion of the mind will be cyberspace, or more specifically the Metaverse, a functional successor to today's 2D Internet, with virtual places instead of Webpages. The Internet and smartphones have enabled the rapid and cheap sharing of information, immersive computing will be able to provide the same for experiences. That means that just as we can read, listen to, and watch videos of anything we want today, soon we'll be able to experience stunning lifelike simulations in virtual reality indistinguishable from our physical world. We'll be walking and actively interacting in the Metaverse, not slavishly staring at the flat screens.

Mia Dand is an instigator. She has created an important platform in AI Ethics that has proven crucial in the times we currently live. Her most recent event, Women in AI Ethics Annual Conference brought together important voices in the current state of diversity in ethics and AI. Founded in 2018, 100 Brilliant Women in AI Ethics (WAIE) list has cultivated an engaged community and has created an emergence of women in research, technology, culture, business – spanning across the globe. What has emanated are the stories and lessons from their important works that have spilled into the mainstream.

A few months ago I was at Johns Hopkins University's Applied Physics Lab in suburban Maryland, where I serve as a senior fellow. A group of us -- mostly retired four-star military officers -- were there to witness a computer-simulated dogfight of a unique character: man against machine. I was seated next to retired Admiral John Richardson, who until last fall had been chief of naval operations, the highest-ranking officer in the fleet. We were both skeptical that the artificial intelligence program that would be piloting one of the virtual aircraft would be able to outfight the human pilot, call sign "Banger," from the Air Force's equivalent of the Navy's legendary TOPGUN fighter-tactics instruction program. It was a remarkable blend of software development, AI, modelling and simulation, combat-aircraft dynamics and controls, and advanced video production -- it felt like watching an ESPN sports event.

A social media platform designed for America's military community is now equipped with a custom machine learning model that insiders say can rapidly review public posts and pinpoint those that show signs and risks of potential self-harm. With support from the Veterans Affairs Department and Harvard University's Nock Lab, Amazon Web Services linked up with the existing RallyPoint military social media platform to target the production of a technological solution that can speedily surface sensitive public posts and boost online suicide intervention. "Historically, the heavy lifting of mental health support on RallyPoint has been shouldered by RallyPoint members stepping up to help each other when they come across people sharing their challenges on our site," RallyPoint CEO Dave Gowel recently told Nextgov. "Now, through our work with the VA, AWS and mental health experts from Harvard, we are more proactive in reinforcing our members' good work by offering helpful resources when we are alerted about public posts showing signs of risk." Launched in 2012, RallyPoint enables nearly 2 million service members, veterans, and their families to connect, share stories and information, ask questions and ultimately chat on topics that accompany military and veteran life.

It turns out humans are not the only creatures that use food as foreplay. Researchers in southwestern Mexico have recorded a group of rough-toothed dolphins sharing a meal and getting frisky. A drone camera caught two dolphins passing a piece of fish back and forth in what may be the first video of the conduct. The repast seemed to inspire some amorous behavior, as well, with two males initiating sexual encounters with another member of the pod. Rough-toothed dolphins spend up to 80 percent of their time in the ocean depths, making them extremely difficult to study.

Discovering high-level causal relations from low-level data is an important and challenging problem that comes up frequently in the natural and social sciences. In a series of papers, Chalupka et al. (2015, 2016a, 2016b, 2017) develop a procedure for causal feature learning (CFL) in an effort to automate this task. We argue that CFL does not recommend coarsening in cases where pragmatic considerations rule in favor of it, and recommends coarsening in cases where pragmatic considerations rule against it. We propose a new technique, pragmatic causal feature learning (PCFL), which extends the original CFL algorithm in useful and intuitive ways. We show that PCFL has the same attractive measure-theoretic properties as the original CFL algorithm. We compare the performance of both methods through theoretical analysis and experiments.

The ocean is filled with microscopic microalgae called phytoplankton, which together are responsible for as much photosynthesis as all plants on land combined. Our ability to predict their response to the warming ocean relies on understanding how the dynamics of phytoplankton populations is influenced by changes in environmental conditions. One powerful technique to study the dynamics of phytoplankton is flow cytometry, which measures the optical properties of thousands of individual cells per second. Today, oceanographers are able to collect flow cytometry data in real-time onboard a moving ship, providing them with fine-scale resolution of the distribution of phytoplankton across thousands of kilometers. One of the current challenges is to understand how these small and large scale variations relate to environmental conditions, such as nutrient availability, temperature, light and ocean currents. In this paper, we propose a novel sparse mixture of multivariate regressions model to estimate the time-varying phytoplankton subpopulations while simultaneously identifying the specific environmental covariates that are predictive of the observed changes to these subpopulations. We demonstrate the usefulness and interpretability of the approach using both synthetic data and real observations collected on an oceanographic cruise conducted in the north-east Pacific in the spring of 2017.

"We head to the Pacific northwest to understand the obstacles that confront these endangered orcas and how researchers are using artificial intelligence to help orcas and humans to coexist. WHAT HAPPENED TO J thirty five or Tala wasn't an anomaly the southern resident cavs have been struggling to survive for some time they've been listed as endangered in both the US and Canada since the mid arts. But their numbers continue to fall in two, thousand five there were eight. Now there are just seventy two in the wild one lives in captivity. Their home waters in the sailor, see an elaborate network of channels that span the coasts of Seattle Vancouver from Olympia Washington in the south to the middle of Vancouver Island British Columbia in the north. The see encompasses puget sound the Strait of Georgia and the Strait of Juan De. Much of it is rich in natural beauty and teeming with wildlife with rural shorelines backlit by tall evergreens and craggy.