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The recent crisis has increased focus on autonomous robots being used for practical benefit. Weve seen robots cleaning hospitals, delivering food and medicines and even assessing patients. These are all amazing use cases, and clearly illustrate the ways in which robots will play a greater role in our lives from now on.

However, for all their benefits, currently the ability for a robot to autonomously map its surroundings and successfully locate itself is still quite limited. Robots are getting better at doing specific things in planned, consistent environments; but dynamic, untrained situations remain a challenge.

Age of PerceptionWhat excites me is the next generation of SLAM (Simultaneous Localization and Mapping) that will allow robot designers to create robots much more capable of autonomous operation in a broad range of scenarios. It is already under development and attracting investment and interest across the industry.

We are calling it the Age of Perception, and it combines recent advances in machine and deep learning to enhance SLAM. Increasing the richness of maps with semantic scene understanding improves localization, mapping quality and robustness.

Simplifying MapsCurrently, most SLAM solutions take raw data from sensors and use probabilistic algorithms to calculate the location and a map of the surroundings of the robot. LIDAR is most commonly used but increasingly lower-cost cameras are providing rich data streams for enhanced maps. Whatever sensors are used the data creates maps made up of millions of 3-dimensional reference points. These allow the robot to calculate its location.

The problem is that these clouds of 3D points have no meaning they are just a spatial reference for the robot to calculate its position. Constantly processing all of these millions of points is also a heavy load on the robots processors and memory. By inserting machine learning into the processing pipeline we can both improve the utility of these maps and simplify them.

Panoptic SegmentationPanoptic Segmentation techniques use machine learning to categorize collections of pixels from camera feeds into recognizable objects. For example, the millions of pixels representing a wall can be categorized as a single object. In addition, we can use machine learning to predict the geometry and the shape of these pixels in the 3D world. So, millions of 3D points representing a wall can be all summarized into a single plane. Millions of 3D points representing a chair can be all summarized into a shape model with a small number of parameters. Breaking scenes down into distinct objects into 2D and 3D lowers the overhead on processors and memory.

What excites me is the next generation of SLAM that will allow robot designers to create robots much more capable of autonomous operation in a broad range of scenarios. It is already under development and attracting investment and interest across the industry.

Adding UnderstandingAs well as simplification of maps, this approach provides the foundation of greater understanding of the scenes the robots sensors capture. With machine learning we are able to categorize individual objects within the scene and then write code that determines how they should be handled.

The first goal of this emerging capability is to be able to remove moving objects, including people, from maps. In order to navigate effectively, robots need to reference static elements of a scene; things that will not move, and so can be used as a reliable locating point. Machine learning can be used to teach autonomous robots which elements of a scene to use for location, and which to disregard as parts of the map or classify them as obstacles to avoid. Combining the panoptic segmentation of objects in a scene with underlying map and location data will soon deliver massive increases in accuracy and capability of robotic SLAM.

Perceiving ObjectsThe next exciting step will be to build on this categorization to add a level of understanding of individual objects. Machine learning, working as part of the SLAM system, will allow a robot to learn to distinguish the walls and floors of a room from the furniture and other objects within it. Storing these elements as individual objects means that adding or removing a chair will not necessitate the complete redrawing of the map.

This combination of benefits is the key to massive advances in the capability of autonomous robots. Robots do not generalize well in untrained situations; changes, particularly rapid movement, disrupt maps and add significant computational load. Machine learning creates a layer of abstraction that improves the stability of maps. The greater efficiency it allows in processing data creates the overhead to add more sensors and more data that can increase the granularity and information that can be included in maps.

Machine learning can be used to teach autonomous robots which elements of a scene to use for location, and which to disregard as parts of the map or classify them as obstacles to avoid.

Natural InteractionLinking location, mapping and perception will allow robots to understand more about their surroundings and operate in more useful ways. For example, a robot that can perceive the difference between a hall and a kitchen can undertake more complex sets of instructions. Being able to identify and categorize objects such as chairs, desks, cabinets etc will improve this still further. Instructing a robot to go to a specific room to get a specific thing will become much simpler.

The real revolution in robotics will come when robots start interacting more with people in more natural ways. Robots that learn from multiple situations and combine that knowledge into a model that allows them to take on new, un-trained tasks based on maps and objects preserved in memory. Creating those models and abstraction demands complete integration of all three layers of SLAM. Thanks to the efforts of the those who are leading the industry in these areas, I believe that the Age of Perception is just around the corner.

Editors Note: Robotics Business Review would like to thank SLAMcore for permission to reprint the original article (found HERE).

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SLAM + Machine Learning Ushers in the "Age of Perception - Robotics Business Review

Smartphones today have become so powerful that sometimes even mid-range handsets can support some fancy machine learning and AI applications. Most of those, however, still rely on cloud-hosted neural networks, machine learning models, and processing, which has both privacy and efficiency drawbacks. Contrary to what most would expect, Google has been moving to offload much of that machine learning activity from the cloud to the device and its latest machine learning development tool is its latest step in that direction.

Googles machine learning or ML Kit SDK has been around for two years now but it has largely been tied to its Firebase mobile and web development platform. Like many Google products, this creates a dependency on a cloud-platform that entails not just some latency due to network bandwidth but also risks leaking potentially private data in transit.

While Google is still leaving that ML Kit + Firebase combo available, it is now also launching a standalone software development kit or SDK for both Android and iOS app developers that focuses on on-device machine learning. Since everything happens locally, the users privacy is protected and the app can function almost in real-time regardless of the speed of the Internet connection. In fact, an ML-using app can even work offline for that matter.

The implications of this new SDK can be quite significant but it still depends on developers switching from the Firebase version to the standalone SDK. To give them a hand, Google created a code lab that combines the new ML Kit with its CameraX app in order to translate text in real-time without connecting to the Internet.

This can definitely help boost confidence in AI-based apps if the user no longer has to worry about privacy or network problems. Of course, Google would probably prefer that developers keep using the Firebase connection which it even describes as getting the best of both products.

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Googles new ML Kit SDK keeps all machine learning on the device - SlashGear

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There isnt a new story every week about an interesting new application of artificial intelligence and machine learning happening out there somewhere. There are actually at least five of those stories. Maybe 10. Sometimes, even more.

Like how UK officials are using AI tospot invasive plant species and stop thembefore they cause expensive damage to roads. Or how artificial intelligence is playing a key role inthe fight against COVID-19. Or even in the ultimate in mind-bending Black Mirror-type ideas, how AI is actually being used to help tobuild and manageother AIs.

Scariness aside, the power of artificial intelligence and machine learning to revolutionize the planet is taking hold in virtually every industry imaginable. With implications like that, it isnt hard to understand how a computer science type trained in AI practices can become a key member of any business witha paycheck to match.

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AI and Machine Learning Are Changing Everything. Here's How You Can Get In On The Fun - ExtremeTech

By applying unsupervised and automated machine learning techniques to the analysis of millions of cancer cells, Rebecca Ihrie and Jonathan Irish, both associate professors of cell and developmental biology, have identified new cancer cell types in brain tumors. Machine learning is a series of computer algorithms that can identify patterns within enormous quantities of data and get smarter with more experience. This finding holds the promise of enabling researchers to better understand and target these cell types for research and therapeutics for glioblastoma an aggressive brain tumor with high mortality as well as the broader applicability of machine learning to cancer research.

With their collaborators, Ihrie and Irish developed Risk Assessment Population IDentification (RAPID), an open-source machine learning algorithm that revealed coordinated patterns of protein expression and modification associated with survival outcomes.

The article, Unsupervised machine learning reveals risk stratifying glioblastoma tumor cells was published online in the journal eLife on June 23. RAPID code and examples are available on the cytolab Github page.

For the past decade, the research community has been working to leverage machine learnings ability to absorb and analyze more data for cancer cell research than the human mind alone can process. Without any human oversight, RAPID combed through 2 million tumor cells with at least 4,710 glioblastoma cells from each patient from 28 glioblastomas, flagging the most unusual cells and patterns for us to look into, said Ihrie. Were able to find the needles in the haystack without searching the entire haystack. This technology lets us devote our attention to better understanding the most dangerous cancer cells and to get closer to ultimately curing brain cancer.

Fed into RAPID were data on cellular proteins that govern the identity and function of neural stem cells and other brain cells. The data type used is called single-cell mass cytometry, a measurement technique typically applied to blood cancer. Once RAPIDs statistical analysis was complete and the needles in the haystack were found, only those cells were studied. One of the most exciting results of our research is that unsupervised machine learning found the worst offender cells without needing the researchers to give it clinical or biological knowledge as context, said Irish, also scientific director of Vanderbilts Cancer & Immunology Core. The findings of this study currently represent the biggest biology advance from my lab at Vanderbilt.

The researchers machine learning analysis enabled their team to study multiple characteristics of the proteins in brain tumor cells in relation to other characteristics, delivering new and unexpected patterns. The collaboration between our two labs, the support that we received for this high-risk work from Vanderbilt and the Vanderbilt-Ingram Cancer Center (VICC) and the fruitful collaboration with neurosurgeons and pathologists who provided a unique opportunity to study human cells right out of the brain allowed us to achieve this milestone, said Ihrie and Irish in a joint statement. The co-first authors of the paper are former Vanderbilt graduate students Nalin Leelatian, a current neuropathology resident at Yale (Irish lab), and Justine Sinnaeve (Ihrie lab). Through her research and work on this topic, Leelatian earned the American Brain Tumor Association (ABTA) Scholar-in-Training Award, American Association for Cancer Research (AACR) in April 2017.

The applicability of this research extends beyond cancer research to data analysis techniques for broader human disease research and laboratory modeling of diseases using multiple samples. The paper also demonstrates that these complex patterns, once found, can be used to develop simpler classifications that can be applied to hundreds of samples. Researchers studying glioblastoma brain tumors will be able to refer to these findings as they test to see if their own samples are comparable to the cell and protein expression patterns discovered by Ihrie, Irish, and collaborators.

This work was supported by the Michael David Greene Brain Cancer Fund, a discovery grant for brain tumor research established in 2004. The grant was recently renewed for another five years to support Ihrie and Irishs continued research on glioblastoma. Additional support was provided by the National Institutes of Health, including the National Cancer Institute and National Institute of Neurological Disorders and Stroke, VICC and VICC Ambassadors, the Vanderbilt International Scholars program, a Vanderbilt University Discovery Grant, an Alpha Omega Alpha Postgraduate Award, a Society of Neurological Surgeons/RUNN Award, a Burroughs Wellcome Fund Physician-Scientist Institutional Award, the Vanderbilt Institute for Clinical and Translational Research, and the Southeastern Brain Tumor Foundation.

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Discovery of aggressive cancer cell types by Vanderbilt researchers made possible with machine learning techniques - Vanderbilt University News

Economists use the term hysteresis to describe the phenomenonthat, when conditions in an economy change, the effects of that change often remain even after the conditions return to normal.

COVID and its impact on the workforce may provide a good example of hysteresis, said HAI Distinguished Fellow and MIT professor Erik Brynjolfsson, who will join Stanford faculty in July 2020 as the director of the new Digital Economy Lab.

To keep workers safe and continue functioning, companies have ramped up remote work and are aggressively automating someoperations and exploring machine learning.

Some of these changes are going to be permanent, he said during Stanford HAIs recent online conference COVID+AI: The Road Ahead. The question is, what parts of the economy are going to be most affected by the adoption of these technologies, andwhich parts will be less affected?

Brynjolfsson worked with Carnegie Mellon professor Tom Mitchell, MIT postdoc Daniel Rock, and others on a series of papers identifying the tasks most suitable for machine learning (ML). They applied this rubric to 950 occupations and 18,000 specific occupation tasks.

More tasks in lower-wage jobs could be replaced by machine learning applications, they found. For example, ML today can better recognize a cucumber or a banana and handle some cashier skills. But some high-paid jobs can also be impacted, such as airline pilots. No occupation is completely immune, Brynjolfsson said.

Certain industries are also more impacted than others, he noted. Manufacturing, retailing, transportation, and accommodation and food services have many tasks suitable for machine learning.

Additionally, different areas of the country will be affected unevenly. The kinds of work that people do in Wyoming are very different from what they do in Manhattan or Miami, he said.

The researchers data also allowed them to examine ML impact on individual occupations. Roles like tellers, executive assistants, and personal bankers have a large percentage of tasks that are suitable for ML.

Our tool gives them a way to have a path for what to do next, Brynjolfsson said. Personal bankers could develop more skills not subject to machine learning, like leadership, product development, or customer relations, and move away from the skills more suitable to ML like credit authorization. Another option: Find new roles with similar skill sets. A personal banker might transition to business analyst or mortgage loan officer, roles ML is less likely to disrupt.

With a little bit of training, they're in a position to be much less vulnerable to the machine learning revolution, he noted.

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How Work Will Change Following the Pandemic - Stanford University News