For decades, artificial intelligence and machine learning have advanced at a rapid pace. Today, there are many ways artificial intelligence and machine learning are used behind the scenes to impact our everyday lives, such as social media, shopping recommendations, email spam detection, speech recognition, self-driving cars, UAVs, and so on.

The simulation of human intelligence is programmed to think like humans and mimic our actions to achieve a specific goal. In our own field, machine learning has also changed the ways to solve navigation problems and taken on a significant role in advancing PNT technologies in the future.

LI-TA HSU, HONG KONG POLYTECHNIC UNIVERSITY

Q: Can machine learning replace conventional GNSS positioning techniques?

Actually, it makes no sense to use ML when the exact physics/mathematical models of GNSS positioning are known, and when using machine learning (ML) techniques over any appreciable area to collect extensive data and train the network to estimate receiver locations would be an impractically large undertaking. We, human beings, designed the satellite navigation systems based on the laws of physics discovered. For example, we use Keplers laws to model the position of satellites in an orbit. We use the spread-spectrum technique to model the satellite signal allowing us to acquire very weak signals transmitted from the medium-Earth orbits. We understand the Doppler effect and design tracking loops to track the signal and decode the navigation message. We finally make use of trilateration to model the positioning and use the least square to estimate the location of the receiver. By the efforts of GNSS scientists and engineers for the past several decades, GNSS can now achieve centimeter-level positioning. The problem is; if everything is so perfect, why dont we have a perfect GNSS positioning?

The answer for me as an ML specialist is that the assumptions made are not always valid in all contexts and applications! In trilateration, we assume the satellite signal always transmitted in direct line-of-sight (LOS). However, different layers in the atmosphere can diffract the signal. Luckily, remote-sensing scientists studied the troposphere and ionosphere and came up with sophisticated models to mitigate the ranging error caused by transmission delay. But the multipath effects and non-line-of-sight (NLOS) receptions caused by buildings and obstacles on the ground are much harder to deal with due to their high nonlinearity and complexity.

Q: What are the challenges of GNSS and how can machine learning help with it?

GNSS performs very differently under different contexts. Context means what and where. For example, a pedestrian walks in an urban canyon or a pedestrian sits in a car that drives in a highway. The notorious multipath and NLOS play major roles to affect the performance GNSS receiver under different context. If we follow the same logic of the ionospheric research to deal with the multipath effect, we need to study 3D building models which is the main cause of the reflections. Extracting from our previous research, the right of Figure 1 is simulated based on the LOD1 building model and single-reflected ray-tracing algorithm. It reveals the positioning error caused by the multipath and NLOS is highly site-dependent. In other words, the nonlinearity and complexity of multipath and NLOS are very high.

Generally speaking, ML derives a model based on data. What exactly does ML do best?

Phenomena we simply do not know how to model by explicit laws of physics/math, for example, contexts and semantics.

Phenomena with high complexity, time variance and nonlinearity.

Looking at the challenges of GNSS multipath and the potential of ML, it becomes straightforward to apply artificial intelligence to mitigate multipath and NLOS. One mainstream idea is to use ML to train the models to classify LOS, multipath and NLOS measurements. This idea is illustrated in Figure 2. Three-steps, data labeling, classifier training, and classifier evaluation, are required. In fact, there are also challenges in each step.

Are we confident in our labeling?

In our work, we use 3D city models and ray-tracing simulation to label the measurements we received from the GNSS receiver. The label may not be 100% correct since the 3D models are not conclusive enough to represent the real world. Trees and dynamic objects (vehicles and pedestrians) are not included. In addition, the multiple reflected signals are very hard to trace and the 3D models could have errors.

What are the classes and features?

For the classes, popular selections are the presence (binary) of multipath or NLOS and their associated pseudorange errors. The features are selected based on the variables that are affected by multipath, including carrier-to-noise ratio, pseudorange residual, DOP, etc. If we can assess a step deeper into the correlator, the shape of correlators in code and carrier are also excellent features. Our study evaluates the comparison between the different levels (correlator, RINEX, and NMEA) of features for the GNSS classifier and reveals that the rawer the feature it is, the better classification accuracy can be obtained. Finally, the methods of exploratory data analysis, such as principle component analysis, can better select the features that are more representative to the class.

Are we confident that the data we used to train the classifier are representative enough for the general application cases?

Overfitting of the data is always being a challenge for ML. Multipath and NLOS effects are very difficult in different cities. For example, the architectures in Europe and Asia are very different, producing different multipath effects. Classifiers trained using the data in Hong Kong do not necessarily perform well in London. The categorization of cities or urban areas in terms of their effects on GNSS multipath and NLOS is still an open question.

Q: What are the challenges of integrated navigation systems and how can machine learning can help with them?

Seamless positioning has always been the ultimate goal. However, each sensor has a different performance in different areas. Table 1 gives a rough picture. Inertial sensors seem to perform stably in most areas. But the MEMS-INS suffers from drift and is highly affected by the random noise caused by the temperature variations. Naturally, integrated navigation is a solution. The sensor integration, in fact, shall be regarded in both long-term and short-term.

Long-term Sensor SelectionIn the long term, available sensors for positioning are generally more than enough. The determination of the best subsets of sensors to integrate is the question to ask. Consider an example of seamless positioning for a city dweller travelling from home to the office:

Walking on a street to the subway station (GNSS+IMU)

Walking in a subway station (Wi-Fi/BLE+IMU)

Traveling on a subway (IMU)

Walking in an urban area to the office (VPS+ GNSS+ Wi-Fi/BLE+IMU)

This example clearly shows that seamless positioning should integrate different sensors. The selection of the sensors can be done heuristically or by maximizing the observability of sensors. If the sensors are selected heuristically, we must have the ability to know what context the system is operating under. This is one of the best angles for ML to cut in. In fact, the classification of the scenarios or contexts is exactly what ML does best. A recently published journal paper demonstrates how to detect different contexts using smartphone sensors for context-adaptive navigation (Gao and Groves 2020). Sensors in smartphones are used in the models trained by supervised ML to determine not only the environment but also the behavior (such as transportation modes, including static, pedestrian walk, and sitting on a car or a subway, etc.).

According to their result, the state-of-the-art detection algorithm can achieve over 95% for pedestrians under indoor, intermediate, and outdoor scenarios. This finding encourages the use of ML to intelligently select the right navigation systems for an integrated navigation system under different areas. The same methodology can be easily extended to vehicular applications with a proper modification in the selections of features, classes, and machine learning algorithms.

Short-term Sensor Weighting

Technically speaking, an optimal integrated solution can be obtained if the uncertainty of the sensor can be optimally described. Presumably, the sensors uncertainty remains unchanged under a certain environment. As a result, most of the sensors uncertainty is carefully calibrated before its use in integration systems.

However, the problem is that the environment could change rapidly within a short period of time. For example, a car drives in an urban area with several viaducts or a car drives in an open sky with a canopy of foliage. These scenarios affect the performance of GNSS greatly, however, the affecting periods were too short to exclude the GNSS from the subset of sensors used. The best solution against these unexpected and transient effects are de-weighting the affected sensors in the system.

Due to the complexity of these effects, adaptive tuning of the uncertainty based on ML is getting popular. Our team demonstrated this potential by an experiment of a loosely coupled GNSS/INS integration. This experiment took place in an urban canyon with commercial GNSS and MEMS INS. Different ML algorithms are used to classify the GNSS positioning errors into four classes: healthy, slightly shifted, inaccurate, and dangerous. These are represented as 1 to 4 in the bottom of Figure 4. The top and bottom of the figure show the error of the commercial GNSS solution and the predicted classes by different ML. It clearly shows that ML can do a very good job predicting the class of the GNSS solution, enabling the integrated to allocate proper weighting to GNSS. Table 2 shows the improvement made by the ML-aided integration system.

This is just an example to preliminarily show the potential of ML in estimating/predicting sensors uncertainty. The methodology can also be applied to different sensor integration such as Wi-Fi/BLE/IMU integration. The challenge of the trained classifier may be too specific for a certain area due to the over-fitting of the data. This remains an open research question in the field.

Q: Machine Learning or Deep Learning for Navigation Systems?

Based on research in object recognition in computer science, deep learning (DL) is the currently the mainstream method because it generally outperforms ML when two conditions are fulfilled, data and computation. The trained model of DL is completely data-driven, while ML trains models to fit assumed (known) mathematical models. A rule of thumb to select ML or DL is the availability of the data in hand. If extensive and conclusive data are available, DL achieves excellent performance due to its superiority in data fitting. In the other words, DL can automatically discover features that affect the classes. However, a model trained by ML is much more comprehensible compared to that trained by DL. The DL model becomes like a black box. In addition, the nodes and layers of convolution in DL are used to extract features. The selection of the number of layers and the number of nodes is still very hard to determine, so that in trial-and-error approaches are widely adopted. These are the major challenges in DL.

If a DL-trained neutral network can be perfectly designed for the integrated navigation system, then it should consider both long-term and short-term challenges. Figure 5 shows this idea. Several hidden layers will be designed to predict the environments (or contexts) and the others are to predict the sensor uncertainty. The idea is straightforward, whereas the challenges remain:

Are we confident that the data we used to train the classifier are representative enough for the general applications cases?

What are the classes?

What are the features?

How many layers and the number of nodes should be used?

Q: How does machine learning affect the field of navigation?

ML will accelerate the development of seamless positioning. With the presence of ML in the navigation field, a perfect INS is no longer the only solution. These AI technologies facilitate the selection of the appropriate sensors or raw measurements (with appropriate trust) against complex navigation challenges. The transient selection of the sensors (well-known as plug-and-play) will affect the integration algorithm. Integration R&D engineers in navigation have been working on the Kalman filter and its variants. However, the flexibility of the Kalman filter makes it hard to accommodate the plug-and-play of sensors. The graph optimization that is widely used in the robotics field could be a very strong candidate to integrate sensors for navigation purposes.

Other than GNSS and the integrated navigation system mentioned above, the recently developed visual positioning system (VPS) by Google could replace the visual corner point detection by the semantic information that detected by ML. Looking at how we navigated before GNSS, we compare visual landmarks with our memory (database) to infer where we are and where we are heading. ML can segment and classify images taken by a camera into different classes, including building, foliage, road, curb, etc., and compare the distribution of the semantic information with that in the database in the cloud server. If they match, the associated position and orientation tag in the database can be regarded as the user location.

AI technologies are coming. They will influence navigation research and development. In my opinion, the best we can do is to mobilize AI to tackle the challenges to which we currently lack solutions. It is highly probable that technology advances and learning focus will depend greatly on MLs development and achievement in the field of navigation.

References

(1) Groves PD, Challenges of Integrated Navigation, ION GNSS+ 2018, Miami, Florida, pp. 3237-3264.

(2) Gao H, Groves PD. (2020) Improving environment detection by behavior association for context-adaptive navigation. NAVIGATION, 67:4360. https://doi.org/10.1002/navi.349

(3) Sun R., Hsu L.T., Xue D., Zhang G., Washington Y.O., (2019) GPS Signal Reception Classification Using Adaptive Neuro-Fuzzy Inference System, Journal of Navigation, 72(3): 685-701.

(4) Hsu L.T. GNSS Multipath Detection Using a Machine Learning Approach, IEEE ITSC 2017, Yokohama, Japan.

(5) Yozevitch R., and Moshe BB. (2015) A robust shadow matching algorithm for GNSS positioning. NAVIGATION, 62.2: 95-109.

(6) Chen P.Y., Chen H., Tsai M.H., Kuo H.K., Tsai Y.M., Chiou T.Y., Jau P.H. Performance of Machine Learning Models in Determining the GNSS Position Usage for a Loosely Coupled GNSS/IMU System, ION GNSS+ 2020, virtually, September 21-25, 2020.

(7) Suzuki T., Nakano, Y., Amano, Y. NLOS Multipath Detection by Using Machine Learning in Urban Environments, ION GNSS+ 2017, Portland, Oregon, pp. 3958-3967.

(8) Xu B., Jia Q., Luo Y., Hsu L.T. (2019) Intelligent GPS L1 LOS/Multipath/NLOS Classifiers Based on Correlator-, RINEX-and NMEA-Level Measurements, Remote Sensing 11(16):1851.

(9) Chiu H.P., Zhou X., Carlone L., Dellaert F., Samarasekera S., and Kumar R., Constrained Optimal Selection for Multi-Sensor Robot Navigation Using Plug-and-Play Factor Graphs, IEEE ICRA 2014, Hong Kong, China.

(10) Zhang G., Hsu L.T. (2018) Intelligent GNSS/INS Integrated Navigation System for a Commercial UAV Flight Control System, Aerospace Science and Technology, 80:368-380.

(11) Kumar R., Samarasekera S., Chiu H.P., Trinh N., Dellaert F., Williams S., Kaess M., Leonard J., Plug-and-Play Navigation Algorithms Using Factor Graphs, Joint Navigation Conference (JNC), 2012.

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What are the roles of artificial intelligence and machine learning in GNSS positioning? - Inside GNSS

In data, companies trust. By 2021, one in four forward-thinking enterprises will push AI to new frontiers, such as holographic meetings for remote work and on-demand personalised manufacturing, as per new predictions by Forrester Research. Even today, all of us are subconsciously using Machine Learning in our daily lives. Wish to travel? Maps: AI-powered. Wish to stay home and yet be social? Facebook, Snapchat: ML-AI powered.A nascent domain thats roughly 60 years old, has changed the way humans and machines perform, thats for sure.

AI will create 2.3 million jobs in 2020. By 2020, Artificial Intelligence to create more jobs than it eliminates, says Gartner. Todays tech-ready industries already use AI for automated jobs that are highly repeatable, where large quantities of observations and decisions can be analysed for patterns.

To stay relevant and secure an irreplaceable position in your industry, it is important to upskill and be in the know of the latest trends and technologies. The first step in doing so, would be to pursue online programs that allow you to work while you learn. It is extremely crucial to keep in mind that only listening to professors half-mindedly while bingeing in a parallel tab will not cut it. The program that you choose to pursue needs to be as rigorous and engaging as any offline university that you go to. upGrad, Indias largest online higher education company, has collaborated with top national and global universities like IIIT Bangalore, IIT Madras, and Liverpool John Moores University to deliver online Machine Learning programs to working professionals. These programs are 100% online and cover industry-relevant case studies and projects, allowing learners to get practical knowledge along with theoretical comprehension, thanks to best-in-class content and live lectures from industry leaders. Based on your interest, you can choose a format of your choice, be it a PG Diploma, an Advanced Certification, or a Masters degree. With one-on-one mentorship from industry leaders and personalised assistance from dedicated student mentors, upGrad ensures that every learner hits the ground running, as soon as they graduate.

Though the global pandemic is affecting millions of jobs worldwide, according to Indeed, a leading job portal, the demand for AI jobs in India has been on the upswing for five years, and has particularly increased in the past six months. Python, a programming language and Natural Language Processing (NLP), essential to making Artificial Intelligence effective as it is the study of computer and human language interaction are the most high demand skills within AI jobs.*With a rise in demand, the competition rises as well. Stay ahead in your career, online programs from top universities are a few clicks away (thanks to Machine Learning and AI!).

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Machine Learning and AI - What Does The Future Hold? - Analytics Insight

CHICAGO, Dec. 13, 2020 /PRNewswire/ -- Artificial intelligence (AI) has the potential to revolutionize healthcare, but integrating AI-based techniques into routine medical practice has proven to be a significant challenge. A plenary session at the virtual 2020 AACC Annual Scientific Meeting & Clinical Lab Expo will explore how one clinical lab overcame this challenge to implement a machine learning-based test, while a second session will take a big picture look at what machine learning is and how it could transform medicine.

Machine learning is a type of AI that uses statistics to find patterns in massive amounts of data. It could launch healthcare into a new era by mining medical data to find cures for diseases, identify vulnerable patients before they become ill, and better personalize testing and treatments. In spite of this technology's promise, though, the medical community continues to grapple with numerous barriers to adoption, and in the field of laboratory medicine in particular, very few machine learning tests are currently offered as part of regular care.

A 10-year machine learning project undertaken by Ulysses G.J. Balis, MD, and his colleagues at the University of Michigan in Ann Arbor could help to change this by providing a blueprint for other healthcare institutions looking to harness AI. As Dr. Balis will discuss in his plenary session, his institute developed and implemented a machine learning test called ThioMon to guide treatment of inflammatory bowel disease (IBD) with azathioprine. With an approximate cost of only $20 a month, azathioprine is much cheaper than other IBD medications (which can cost thousands of dollars a month), but its dosage needs to be finetuned for each patient, making it difficult to prescribe. ThioMon solves this issue by analyzing a patient's routine lab test results to determine if a particular dose of azathioprine is working or not.

Balis's team found that the test performs just as well as a colonoscopy, which is the current gold standard for assessing IBD patient response to medication. Even more exciting is that clinical labs could use ThioMon's general approachanalyzing routine lab test results with machine learning algorithmsto solve any number of other patient care challenges.

"There are dozens, if not hundreds of additional diagnoses that we can extract from the routine lab values that we've been generating for decades," said Dr. Balis. "This lab data is, in essence, a gold mine, and the development of these machine learning tools marks the start of a new gold rush."

One of the additional conditions that this machine learning approach can diagnose is, in fact, COVID-19. In the session, "How Clinical Laboratory Data Is Impacting the Future of Healthcare?" Jonathan Chen, MD, PhD, of Stanford University, and Christopher McCudden, PhD, of the Eastern Ontario Regional Laboratory Association, will touch on a new machine learning test that analyzes routine lab test results to determine if patients have COVID-19 even before their SARS-CoV-2 test results come back. As COVID-19 cases in the U.S. reach record highs, this test could enable labs to diagnose COVID-19 patients quickly even if SARS-CoV-2 test supply shortages worsen or if SARS-CoV-2 test results become backlogged due to demand.

Beyond this, Drs. Chen and McCudden plan to give a bird's eye view of what machine learning is, how it works, and how it can improve efficiency, reduce costs, and improve patient outcomesparticularly by democratizing patient access to medical expertise.

"Medical expertise is the scarcest resource in the healthcare system," said Dr. Chen, "and computational, automated tools will allow us to reach the tens of millions of people in the U.S.and the billions of people worldwidewho currently don't have access to it."

Machine Learning Sessions at the 2020 AACC Annual Scientific MeetingAACC Annual Scientific Meeting registration is free for members of the media. Reporters can register online here:https://www.xpressreg.net/register/aacc0720/media/landing.asp

Session 14001: Between Scylla and Charybdis: Navigating the Complex Waters of Machine Learning in Laboratory Medicine

Session 34104: How Clinical Laboratory Data Is Impacting the Future of Healthcare?

Abstract A-005: Machine Learning Outperforms Traditional Screening and Diagnostic Tools for the Detection of Familial Hypercholesterolemia

About the 2020 AACC Annual Scientific Meeting & Clinical Lab ExpoThe AACC Annual Scientific Meeting offers 5 days packed with opportunities to learn about exciting science from December 13-17, all available on an online platform. This year, there is a concerted focus on the latest updates on testing for COVID-19, including a talk with current White House Coronavirus Task Force testing czar, Admiral Brett Giroir. Plenary sessions include discussions on using artificial intelligence and machine learning to improve patient outcomes, new therapies for cancer, creating cross-functional diagnostic management teams, and accelerating health research and medical breakthroughs through the use of precision medicine.

At the virtual AACC Clinical Lab Expo, more than 170 exhibitors will fill the digital floor with displays and vital information about the latest diagnostic technology, including but not limited to SARS-CoV-2 testing, mobile health, molecular diagnostics, mass spectrometry, point-of-care, and automation.

About AACCDedicated to achieving better health through laboratory medicine, AACC brings together more than 50,000 clinical laboratory professionals, physicians, research scientists, and business leaders from around the world focused on clinical chemistry, molecular diagnostics, mass spectrometry, translational medicine, lab management, and other areas of progressing laboratory science. Since 1948, AACC has worked to advance the common interests of the field, providing programs that advance scientific collaboration, knowledge, expertise, and innovation. For more information, visit http://www.aacc.org.

Christine DeLongAACCSenior Manager, Communications & PR(p) 202.835.8722[emailprotected]

Molly PolenAACCSenior Director, Communications & PR(p) 202.420.7612(c) 703.598.0472[emailprotected]

SOURCE AACC

http://www.aacc.org

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Artificial Intelligence Advances Showcased at the Virtual 2020 AACC Annual Scientific Meeting Could Help to Integrate This Technology Into Everyday...

School of Computer Science

Location: LincolnSalary: From 33,797 per annumThis post is full time and fixed term until 13 August 2021Closing Date: Sunday 10 January 2021Interview Date: Thursday 28 January 2021Reference: COS707B

The University of Lincoln is seeking to appoint a Research Associate. The position is funded by the Ceres Agri-Tech Knowledge Exchange Partnership, which aims to build a second-generation robotic with advanced stereovision in conjunction with a novel high tack surface gripper/end effector.

In our previous project, a UoL team, which included LMF Mushrooms and Stelram Engineering, successfully built a picking prototype robot that can pick individual upright mushrooms with minimal damage. The system was operated by a combination of novel soft robotic actuators and an advanced tracking system driven by powerful 3D perception algorithms. The problem that this project will try to solve is picking mushrooms that grow in highly complex and biologically variable clusters. There is a lack of a simple universal grasping actuator to pick mushrooms without damage, as well as the need to develop powerful 3D perception algorithms to target mushrooms and to integrate this into motion planning and control systems. This project will attempt to solve these issues by highly novel soft robotic actuators deployed in combination with advanced guidance and tracking systems operating within a 3D vision sensed environment.

This project has the potential to change the mushroom sector and the application of soft robotics combined with novel tracking algorithms has the capability to underpin the wider deployment of RAS in multiple sectors of food and manufacturing.

We are looking to recruit a postdoctoral Research Associate specialised in the following:

The successful candidate will contribute to the University's ambition to achieve international recognition as a research-intensive institution and will be expected to design, conduct and manage original research in the above subject areas as well contribute to the wider activities of Lincoln School of Computer Science. Evidence of authorship of research outputs of international standing is essential, as is the ability to work collaboratively as part of a team, including excellent written and spoken communication skills. Opportunities to mentor and co-supervise PhD students working in the project team will also be available to outstanding candidates.

Informal enquiries about the post can be made to Dr Bashir Al-Diri (email: baldiri@lincoln.ac.uk).

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Research Associate in Computer Vision and Machine Learning for Robotics job with UNIVERSITY OF LINCOLN | 238417 - Times Higher Education (THE)

Patently Apple has been covering the latest Project Titan patents for years, including a granted patent report posted this morning covering another side of LiDAR that was never covered before. While some in the industry have doubted Apple will ever do anything with this project, Apple has now reportedly moved its self-driving car unit under the leadership of top artificial intelligence executive John Giannandrea, who will oversee the companys continued work on an autonomous system that could eventually be used in its own car.

Bloomberg's Mark Gurman is reporting today that Project Titan is run day-to-day by Doug Field. His team of hundreds of engineers have moved to Giannandreas artificial intelligence and machine-learning group, according to people familiar with the change.

Previously, Field reported to Bob Mansfield, Apples former senior vice president of hardware engineering. Mansfield has now fully retired from Apple, leading to Giannandrea taking over. Mansfield oversaw a shift from the development of a car to just the underlying autonomous system.

In 2017, Patently Apple posted a report titled "Apple's CEO Confirms Project Titan is the 'Mother of all AI Projects' Focused on Self-Driving Vehicles." For more read the full Bloomberg report.

Like with all major Apple projects, be it for a head-mounted display device, smartglasses, folding devices, Apple keeps its secrets and prototypes under wraps until they've holistically worked out their roadmap.

That's why following Apple's patents is the best way to keep on top of the technology that Apple's engineers are actually working on in some capacity within the various ongoing projects. Review our Project Titan patent archive to see what Apple has been working on.

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Apple's SVP of Machine Learning & AI John Giannandrea has been assigned to Oversee Apple's Secretive 'Project Titan' - Patently Apple

These eight women are at the forefront of the field of artificial intelligence today. They hail from ... [+] academia, startups, large technology companies, venture capital and beyond.

It is a simple truth: the field of artificial intelligence is far too male-dominated. According to a 2018 study from Wired and Element AI, just 12% of AI researchers globally are female.

Artificial intelligence will reshape every corner of our lives in the coming yearsfrom healthcare to finance, from education to government. It is therefore troubling that those building this technology do not fully represent the society they are poised to transform.

Yet there are many brilliant women at the forefront of AI today. As entrepreneurs, academic researchers, industry executives, venture capitalists and more, these women are shaping the future of artificial intelligence. They also serve as role models for the next generation of AI leaders, reflecting what a more inclusive AI community can and should look like.

Featured below are eight of the leading women in the field of artificial intelligence today.

Joy Buolamwini has aptly been described as the conscience of the A.I. revolution.

Her pioneering work on algorithmic bias as a graduate student at MIT opened the worlds eyes to the racial and gender prejudices embedded in facial recognition systems. Amazon, Microsoft and IBM each suspended their facial recognition offerings this year as a result of Buolamwinis research, acknowledging that the technology was not yet fit for public use. Buolamwinis work is powerfully profiled in the new documentary Coded Bias.

Buolamwini stands at the forefront of a burgeoning movement to identify and address the social consequences of artificial intelligence technology, a movement she advances through her nonprofit Algorithmic Justice League.

Buolamwini on the battle against algorithmic bias: When I started talking about this, in 2016, it was such a foreign concept. Today, I cant go online without seeing some news article or story about a biased AI system. People are just now waking up to the fact that there is a problem. Awareness is goodand then that awareness needs to lead to action. That is the phase that were in.

From SRI to Google to Uber to NVIDIA, Claire Delaunay has held technical leadership roles at many of Silicon Valleys most iconic organizations. She was also co-founder and engineering head at Otto, the pedigreed but ill-fated autonomous trucking startup helmed by Anthony Levandowski.

In her current role at NVIDIA, Delaunay is focused on building tools and platforms to enable the deployment of autonomous machines at scale.

Delaunay on the tradeoffs between working at a big company and a startup: Some kinds of breakthroughs can only be accomplished at a big company, and other kinds of breakthroughs can only be accomplished at a startup. Startups are very good at deconstructing things and generating discontinuous big leaps forward. Big companies are very good at consolidating breakthroughs and building out robust technology foundations that enable future innovation.

Rana el Kaliouby has dedicated her career to making AI more emotionally intelligent.

Kaliouby is credited with pioneering the field of Emotion AI. In 2009, she co-founded the startup Affectiva as a spinout from MIT to develop machine learning systems capable of understanding human emotions. Today, the companys technology is used by 25% of the Fortune 500, including for media analytics, consumer behavioral research and automotive use cases.

Kaliouby on her big-picture vision: My lifes work is about humanizing technology before it dehumanizes us.

Daphne Kollers wide-ranging career illustrates the symbiosis between academia and industry that is a defining characteristic of the field of artificial intelligence.

Koller has been a professor at Stanford since 1995, focused on machine learning. In 2012 she co-founded education technology startup Coursera with fellow Stanford professor and AI leader Andrew Ng. Coursera is today a $2.6 billion ed tech juggernaut.

Kollers most recent undertaking may be her most ambitious yet. She is the founding CEO at insitro, a startup applying machine learning to transform pharmaceutical drug discovery and development. Insitro has raised roughly $250 million from Andreessen Horowitz and others and recently announced a major commercial partnership with Bristol Myers Squibb.

Koller on advice for those just starting out in the field of AI: Pick an application of AI that really matters, that is really societally worthwhilenot all AI applications areand then put in the hard work to truly understand that domain. I am able to build insitro today only because I spent 20 years learning biology. An area I might suggest to young people today is energy and the environment.

Few individuals have left more of a mark on the world of AI in the twenty-first century than Fei-Fei Li.

As a young Princeton professor in 2007, Li conceived of and spearheaded the ImageNet project, a database of millions of labeled images that has changed the entire trajectory of AI. The prescient insight behind ImageNet was that massive datasetsmore than particular algorithmswould be the key to unleashing AIs potential. When Geoff Hinton and team debuted their neural network-based model trained on ImageNet at the 2012 ImageNet competition, the modern era of deep learning was born.

Li has since become a tenured professor at Stanford, served as Chief Scientist of AI/ML at Google Cloud, headed Stanfords AI lab, joined the Board of Directors at Twitter, cofounded the prominent nonprofit AI4ALL, and launched Stanfords Human-Centered AI Institute (HAI). Across her many leadership positions, Li has tirelessly advocated for a more inclusive, equitable and human approach to AI.

Li on why diversity in AI is so important: Our technology is not independent of human values. It represents the values of the humans that are behind the design, development and application of the technology. So, if were worried about killer robots, we should really be worried about the creators of the technology. We want the creators of this technology to represent our values and represent our shared humanity.

Anna Patterson has led a distinguished career developing and deploying AI products, both at large technology companies and at startups.

A long-time executive at Google, which she first joined in 2004, Patterson led artificial intelligence efforts for years as the companys VP of Engineering. In 2017 she launched Googles AI venture capital fund Gradient Ventures, where today she invests in early-stage AI startups.

Patterson serves on the board of a number of promising AI startups including Algorithmia, Labelbox and test.ai. She is also a board director at publicly-traded Square.

Patterson on one question she asks herself before investing in any AI startup: Do I find myself constantly thinking about their vision and mission?

Daniela Rus is one of the worlds leading roboticists.

She is an MIT professor and the first female head of MITs Computer Science and Artificial Intelligence Lab (CSAIL), one of the largest and most prestigious AI research labs in the world. This makes her part of a storied lineage: previous directors of CSAIL (and its predecessor labs) over the decades have included AI legends Marvin Minsky, J.C.R. Licklider and Rodney Brooks.

Rus groundbreaking research has advanced the state of the art in networked collaborative robots (robots that can work together and communicate with one another), self-reconfigurable robots (robots that can autonomously change their structure to adapt to their environment), and soft robots (robots without rigid bodies).

Rus on a common misconception about AI: It is important for people to understand that AI is nothing more than a tool. Like any other tool, it is neither intrinsically good nor bad. It is solely what we choose to do with it. I believe that we can do extraordinarily positive things with AIbut it is not a given that that will happen.

Shivon Zilis has spent time on the leadership teams of several companies at AIs bleeding edge: OpenAI, Neuralink, Tesla, Bloomberg Beta.

She is the youngest board member at OpenAI, the influential research lab behind breakthroughs like GPT-3. At NeuralinkElon Musks mind-bending effort to meld the human brain with digital machinesZilis works on high-priority strategic initiatives in the office of the CEO.

Zilis on her attitude toward new technology development: Im astounded by how often the concept of building moats comes up. If you think the technology youre building is good for the world, why not laser focus on expanding your tech tree as quickly as possible versus slowing down and dividing resources to impede the progress of others?

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8 Leading Women In The Field Of AI - Forbes

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Bioinformatics. 2020 Nov 6:btaa935. doi: 10.1093/bioinformatics/btaa935. Online ahead of print.

ABSTRACT

MOTIVATION: Gene expression and regulation, a key molecular mechanism driving human disease development, remains elusive, especially at early stages. Integrating the increasing amount of population-level genomic data and understanding gene regulatory mechanisms in disease development are still challenging. Machine learning has emerged to solve this, but many machine learning methods were typically limited to building an accurate prediction model as a black box, barely providing biological and clinical interpretability from the box.

RESULTS: To address these challenges, we developed an interpretable and scalable machine learning model, ECMarker, to predict gene expression biomarkers for disease phenotypes and simultaneously reveal underlying regulatory mechanisms. Particularly, ECMarker is built on the integration of semi- and discriminative-restricted Boltzmann machines, a neural network model for classification allowing lateral connections at the input gene layer. This interpretable model is scalable without needing any prior feature selection and enables directly modeling and prioritizing genes and revealing potential gene networks (from lateral connections) for the phenotypes. With application to the gene expression data of non-small-cell lung cancer patients, we found that ECMarker not only achieved a relatively high accuracy for predicting cancer stages but also identified the biomarker genes and gene networks implying the regulatory mechanisms in the lung cancer development. In addition, ECMarker demonstrates clinical interpretability as its prioritized biomarker genes can predict survival rates of early lung cancer patients (P-value < 0.005). Finally, we identified a number of drugs currently in clinical use for late stages or other cancers with effects on these early lung cancer biomarkers, suggesting potential novel candidates on early cancer medicine.

AVAILABILITYAND IMPLEMENTATION: ECMarker is open source as a general-purpose tool at https://github.com/daifengwanglab/ECMarker.

CONTACT: [emailprotected]

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PMID:33305308 | DOI:10.1093/bioinformatics/btaa935

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ECMarker: interpretable machine learning model identifies gene expression biomarkers predicting clinical outcomes and reveals molecular mechanisms of...

But there are a number of problems. One is that workers on these platforms earn very low wages. We did a study where we followed hundreds of Amazon Mechanical Turk workers for several years, and we found that they were earning around $2 per hour. This is much less than the US minimum wage. There are people who dedicate their lives to these platforms; its their main source of income.

And that brings other problems. These platforms cut off future job opportunities as well, because full-time crowdworkers are not given a way to develop their skillsat least not ones that are recognized. We found that a lot of people dont put their work on these platforms on their rsum. If they say they worked on Amazon Mechanical Turk, most employers wont even know what that is. Most employers are not aware that these are the workers behind our AI.

Its clear you have a real passion for what you do. How did you end up working on this?

I worked on a research project at Stanford where I was basically a crowdworker, and it exposed me to the problems. I helped design a new platform, which was like Amazon Mechanical Turk but controlled by the workers. But I was also a tech worker at Microsoft. And that also opened my eyes to what its like working within a large tech company. You become faceless, which is very similar to what crowdworkers experience. And that really sparked me into wanting to change the workplace.

You mentioned doing a study. How do you find out what these workers are doing and what conditions they face?

I do three things. I interview workers, I conduct surveys, and I build tools that give me a more quantitative perspective on what is happening on these platforms. I have been able to measure how much time workers invest in completing tasks. Im also measuring the amount of unpaid labor that workers do, such as searching for tasks or communicating with an employerthings youd be paid for if you had a salary.

Youve been invited to give a talk at NeurIPS this week. Why is this something that the AI community needs to hear?

Well, theyre powering their research with the labor of these workers. I think its very important to realize that a self-driving car or whatever exists because of people that arent paid minimum wage. While were thinking about the future of AI, we should think about the future of work. Its helpful to be reminded that these workers are humans.

Are you saying companies or researchers are deliberately underpaying?

No, thats not it. I think they might underestimate what theyre asking workers to do and how long it will take. But a lot of the time they simply havent thought about the other side of the transaction at all.

Because they just see a platform on the internet. And its cheap.

Yes, exactly.

What do we do about it?

Lots of things. Im helping workers get an idea how long a task might take them to do. This way they can evaluate if a task is going to be worth it. So Ive been developing an AI plug-in for these platforms that helps workers share information and coach each other about which tasks are worth their time and which let you develop certain skills. The AI learns what type of advice is most effective. It takes in the text comments that workers write to each other and learns what advice leads to better results, and promotes it on the platform.

Lets say workers want to increase their wages. The AI identifies what type of advice or strategy is best suited to help workers do that. For instance, it might suggest that you do these types of task from these employers but not these other types of task over there. Or it will tell you not to spend more than five minutes searching for work. The machine-learning model is based on the subjective opinion of workers on Amazon Mechanical Turk, but I found that it could still increase workers wages and develop their skills.

So its about helping workers get the most out of these platforms?

Thats a start. But it would be interesting to think about career ladders. For instance, we could guide workers to do a number of different tasks that let them develop their skills. We can also think about providing other opportunities. Companies putting jobs on these platforms could offer online micro-internships for the workers.

And we should support entrepreneurs. I've been developing tools that help people create their own gig marketplaces. Think about these workers: they are very familiar with gig work and they might have new ideas about how to run a platform. The problem is that they dont have the technical skills to set one up, so Im building a tool that makes setting up a platform a little like configuring a website template.

A lot of this is about using technology to shift the balance of power.

Its about changing the narrative, too. I recently met with two crowdworkers that Ive been talking to and they actually call themselves tech workers, whichI mean, they are tech workers in a certain way because they are powering our tech. When we talk about crowdworkers they are typically presented as having these horrible jobs. But it can be helpful to change the way we think about who these people are. Its just another tech job.

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AI needs to face up to its invisible-worker problem - MIT Technology Review

AutoML (automated machine learning) is an active area of research in academia and the industry. The cloud vendors promote some or the other form of AutoML services. Likewise, Tech unicorns also offer various AutoML services for its platform users. Additionally, many different open source projects are available, offering exciting new approaches.

The growing desire to gain business value from artificial intelligence (AI) has created a gap between the demand for data science expertise and the supply of data scientist. Running AI and AutoML on the latest Intel architecture addresses this challenge by automating many tasks required to develop AI and machine learning applications.

Using AutoML, businesses can automate tedious and time-consuming manual work required by todays data science. With AutoML, data-savvy users of all levels have access to powerful machine learning algorithms to avoid human error.

With better access to the power of ML, businesses can generate advanced machine learning models without the requirement to understand complex algorithms. Data scientists can apply their specialisation to fine-tune ML models for purposes ranging from manufacturing to retailing to healthcare, and more.

With AutoML, the productivity of repetitive tasks can be increased as it enables a data scientist to focus more on the problem rather than the models. Automating ML pipeline also helps to avoid errors that might creep in manually. AutoML is a step towards democratizing ML by making the power of ML accessible to everybody.

Enterprises seek to automate machine learning pipelines and different steps in the ML workflow to address the increase in tendency and requirement for speeding up AI adoption.

Not everything but many things can be automated in the data science workflow. The pre-implemented model types and structures can be presented or learnt from the input datasets for selection. AutoML simplifies the development of projects, proof of value initiatives, and help business users to stimulate ML solutions development without extensive programming knowledge. It can serve as a complementary tool for data scientists that help them to either quickly find out what algorithms they could try or see if they have skipped some algorithms, and that could have been a valuable selection to obtain better outcomes.

Here are some reasons why business leaders should hire data scientists if they have AutoML tools on their hands:

Essentially, the purpose of AutoML is to automate the repetitive tasks like pipeline creation and hyperparameter tuning so data scientists can spend time on the business problem at hand.

AutoML aims to make the technology available to everyone rather a select few. AutoML and data scientists can work in conjunction to speed up the machine learning process to utilise the real effectiveness of ML.

Whether or not AutoML becomes a success depends mainly on its adoption and the advancements that are made in this sector. However, AutoML is a big part of the future of machine learning.

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AutoML is the Future of Machine Learning - Analytics Insight