Golden opportunity: Savvy business alliances propel the robotics sector

The fulfillment economy has exploded during the pandemic, as has competition among automation technology providers, whose robotic technology is becoming critical during widespread labor shortages and ballooning demand.

That's the good news. The bad news, if you're a robotics firm with a great product and opportunity as far as the horizon is that scaling hardware distribution, whether via direct sales or as-a-service, is extremely complex, typically takes massive capital outlays, and is fraught with the perils of miscalculation. What's an emerging robotics firm to do?

One model that's becoming increasingly important for savvy businesses is to partner with an existing brand with a broad reach and pre-existing infrastructure. Examples include Kinova teaming up with Northrop Grumman to help distribute a small manipulator to existing customers and Robotiq partnering with Universal Robots on off-the-shelf robotic tooling.

In the latest example, 6 River Systems, LLC, a leading fulfillment solutions provider, just announced a new initiative to support warehouse efficiencies by teaming up with Ricoh USA. Under the arrangement, RICOH's service solutions business unit will augment 6 River Systems' existing service team for its collaborative robots – called "Chucks," solving for a crucial weakness in any young enterprise technology company's bid to scale: giving customers an ample support network.

"The demand for our automated retail solution is significant, especially with retailers continually looking for ways to get their products into consumers' hands faster via seamless experiences," says Eran Frenkel, Vice President of Technical Operations, 6 River Systems. "By partnering with Ricoh, we're able to focus on making our solutions more widely available, which ultimately helps our customers quickly and efficiently meet their fulfillment goals."

Like other fulfillment automation providers, 6RS is on a bit of a tear during the pandemic. The company has provided solutions for major fulfillers and brands like Crocs, which implemented 6RS' wall-to-wall fulfillment solution, including its collaborative mobile robot Chuck. As I wrote last year, Crocs has seen a 182% pick rate improvement with the 6RS system, illustrating a key reason fulfillers are turning to automation in such numbers. This increase in throughput was especially critical during the holiday peak season.

In general, robots have become essential to scaling, and the solutions can now be brought online with unprecedented speed and minimal downtime. Not surprisingly as according to Statista, the global warehouse automation market is predicted to increase from $15 billion in 2019 to $30 billion by 2026.

But the warehouse automation sector, while maturing rapidly in the Amazon Prime era, is still nascent, with many of the players less than a decade old. That's a short time to build a massive global or even national distribution and support infrastructure. Collaborating seems like a key to efficiently do just that.

"Our collaboration with 6 River Systems is a prime example of how our stable and trusted infrastructure – coupled with a team of more than 10,000 service delivery professionals supporting and maintaining more than one million devices across the U.S. – helps solve our customers' problems," says Jim Kirby, Vice President, Service Advantage, Ricoh USA, Inc. "Together, we are addressing some of the biggest challenges and opportunities in retail today including supply chain operational efficiency such as retail and warehouse automation. By expertly assisting with service and support for companies like 6 River Systems, we are helping them maintain focus on what matters most – innovation that solves supply chain hurdles and moves business forward."

It's a great example of how smart robotics firms are taking advantage of the growth opportunities of 2022 and beyond through effective collaborations designed to scale at speed.

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Serve Robotics Becomes First Autonomous Vehicle Company to Commercially Launch Level 4 Self-Driving Robots

Long-Awaited Industry Milestone Enables Next-Generation Robotic Fleet to Navigate City Sidewalks More Safely and Efficiently

SAN FRANCISCO, Jan. 13, 2022 /PRNewswire/ -- Serve Robotics, the leading autonomous sidewalk delivery company, today announced the deployment of its next-generation delivery robots, becoming the first autonomous vehicle company to complete commercial deliveries at Level 4 autonomy. This milestone means Serve Robotics' latest generation of robots are able to operate routinely without human intervention, and can rely on their onboard capabilities to ensure safe operation. This industry first is the result of nearly five years of work by the Serve Robotics team and represents a major step forward for the autonomous vehicle industry, significantly lowering the barriers for autonomous delivery at scale.


Serve Robotics recently completed the first-ever delivery to occur at Level 4 autonomy, navigating fully autonomously in designated areas. The company's robots are equipped with an extensive array of technologies that ensure the highest degree of safety by utilizing multiple layers of redundant systems for critical navigation functions. This includes multiple sensor modalities—active sensors such as lidar and ultrasonics, as well as passive sensors such as cameras—to navigate safely on busy city sidewalks. Serve Robotics' achievement required development of a wide range of market-leading capabilities, such as automatic emergency braking, vehicle collision avoidance, and fail-safe mechanical braking.


"I'm proud that Serve Robotics has achieved Level 4 autonomy, which further enhances public safety by significantly reducing the potential for human error. This milestone begins to unlock the full potential of robotic delivery," said Serve Robotics co-founder and CEO, Dr. Ali Kashani. "This technical and commercial milestone is an achievement for the entire AV industry, and accelerates our mission to make delivery more accessible and sustainable."

Serve's technical breakthrough was possible with help from key technology partners, including NVIDIA and Ouster. The NVIDIA Jetson platform, designed for robots and other autonomous machines, powers the AI computing necessary for Serve robots to understand their complex environment in real time. Ouster's lidar sensors provide small, lightweight, power-efficient sensing technology that enables the robots' reliable self-driving capabilities.

"Serve Robotics' accomplishment represents a breakthrough for commercial deployment of AV technology for sidewalk delivery," said Murali Gopalakrishna, Head of Product Management, Autonomous Machines and GM for Robotics at NVIDIA. "We look forward to Serve continuing to leverage the NVIDIA Jetson edge AI and Isaac robotics platforms to further advance their technological lead."

"Serve Robotics has achieved a major breakthrough for the AV industry and for sidewalk delivery," said Angus Pacala, CEO of Ouster. "Ouster is pleased to partner with Serve as they continue to scale and bring lidar-powered Level 4 autonomy to doorsteps across the U.S."

Serve Robotics has completed tens of thousands of deliveries in LA. Its fleet of next-generation robots will power the company's expansion into additional geographies as it rolls out delivery service for Uber Eats and other partners in 2022.

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10 PLATFORMS TO GET DATASETS FOR DATA SCIENCE PROJECTS IN 2022

These platforms provide large volumes of information that can be used in data science projects in 2022.

Data science can be interpreted as different things for different roles. Technically the technology revolves around extracting knowledge and insights from data and information generated through various data science tools and applications. Its rising use has made several professionals and aspiring data science professionals create and participate in projects, assignments, including data visualization, data cleaning, and data science projects, along with several machine learning projects. Practicing these projects and assignments can help professionals ace their skills and excel in their careers. In this article, we have listed 10 platforms from where professionals can get datasets for their data science projects in 2022.

• Kaggle: Kaggle is a platform where professionals can learn, practice, and sharpen their data analytics and data science. The platform provides tons of data that are public and allows the users of the platform to share code so that they can learn the best practices within the data space.

• FiveThirtyEight: FiveThirtyEight is an interactive news and sports platform that has some incredible information for data visualization projects. The platform makes a lot of their data available to the public, which means they can download and use the information according to their own convenience.

• Google Dataset Search: Google Dataset Search is one of the most comprehensive dataset search engines that are available. It claims to hold more than 25 million online datasets and assists scientists and researchers in better locating datasets. It is armed with a function, which can sort data types, update dates, and so much more.

• Data.gov: Data.gov allows its users to download and explore data from multiple US government agencies. The information can range from government budgets to climate data. It is documented quite evidently so that it becomes easier for the users to navigate them.

• AWS Public Datasets: AWS Public Datasets allow the users to download the data and work with it on their individual devices. They can analyse the data in the cloud using EC2 and Hadoop via EMR. Amazon has a page that lists all the datasets for its users and also gives free access to all the new accounts.
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• UCI Machine Learning Repository: UCI Machine Learning Repository is one of the oldest sources of datasets on the internet. The datasets are generally contributed by the users, and thus have varying levels of documentation and cleanliness. Users can download UCI Machine Learning Repository without any registration.

• Quandl: Quandl is a repository of economic and financial data. Most of this information is free, but some require purchasing. The platform is extremely useful for building models to predict economic indicators and stock prices.

• data.world: data.world describes itself as the social network for data professionals. It is a platform where they can search for copy, analyze, and download datasets. In addition to this, they can upload their data and use it to collaborate with others.

• Buzzfeed News: Buzzfeed provides datasets, analysis, libraries, tools, and guides that are used in the articles available on GitHub. It is a quite popular platform and is used by millions of data professionals.

• Academic Torrents: Academic Torrents is a new site that is geared around sharing the datasets from specific scientific papers. It is new to the dataset platform market and allows its users to browse data directly on the site.

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NFL Taps Data Science Community To Help Track Head Impacts

NFL taps data science community to help track head impacts


The NFL is continuing to crowdsource new ways to track head and helmet impacts during games from data scientists and for the second straight year the winner of its artificial intelligence competition comes from outside the United States.

The NFL and Amazon Web Services awarded $100,000 in prizes for this year’s competition with the top prize of $50,000 going to Kippei Matsuda from Osaka, Japan, the league announced Friday.

The task for Matsuda and the rest of the data scientists who took part was to use artificial intelligence to create models that would detect helmet impacts from NFL game footage and identify the specific players involved in those impacts.


NFL executive vice president Jeff Miller, who oversees health and safety, said the league started manually tracking helmet impacts for a small number of games a few years ago.

The tedious task of tracking every helmet collision, especially along the line of scrimmage, made it difficult to do more than just a small sampling of games as the league tried to gather more data on head impacts.

By sharing game film and information with the data science community, the league is hoping to continue developing better systems that can track those impacts more efficiently. The league estimates Matsuda’s winning system could detect and track helmet impacts with greater accuracy and 83 times faster than a person working manually.

“There were certainly any number of domestic participants too, but the data science community is large and looking for solutions in places or with communities you wouldn’t normally talk to may end up being a pretty fruitful exercise,” Miller said. “So I think we’ve proven that this model of working with the global data science community is helpful to us and will continue to be and we’ll continue to engage in.”

The first year of the competition in 2020 focused on models that detected all helmet impacts from NFL game footage. That competition was won by Dmytro Poplavskiy from Brisbane, Australia , which included nearly 7,800 submissions from 55 countries.

This year’s competition was focused more on specific player impacts and included 825 teams and 1,028 competitors from 65 countries, and a total of 12,600 submissions.

“This was the most exciting competition I’ve ever experienced,” Matsuda said in a statement. “It’s a very common task for computer vision to detect 2D images, but this challenge required us to consider higher dimensional data such as the 3D location of players on the field. NFL videos are also fun to watch, which is very important since we need to see the data again and again during competition. I would be honored if my AI can help improve the safety of NFL players.”

Miller said the goal of the league is to create a “digital athlete” that can become a virtual representation of the actions, movements and impacts an NFL player experiences on the field during a game and can be used to help predict and hopefully prevent injury in the future.

“That is novel for us and obviously has great importance in how we think about making the game safer for the athletes,” Miller said “It will have an effect on training and coaching, certainly. It will have an effect in rules without a doubt. It will definitely have an impact in terms of equipment, and benefits that we can see from equipment because now for the first time we’ll have a pretty good appreciation for every time somebody hits their head during the course of an NFL game, and therefore, we will look for ways to prevent many of those.”

Priya Ponnapalli, senior manager with Amazon’s Machine Learning Solutions Lab said the potential for machine learning to analyze past data but also make forward-looking projections will be helpful in the future in helping create a digital version of players at all positions and analyze the types of hits they take.

“Machine learning is a very intuitive process and you get to a certain level of performance, and in this case we’ve got some pretty accurate and comprehensive models,” Ponnapalli said. “And as we collect more data, these models are going to get better and better.”

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Physics and the machine-learning “black box”

In 2.C01, George Barbastathis demonstrates how mechanical engineers can use their knowledge of physical systems to keep algorithms in check and develop more accurate predictions.

Machine-learning algorithms are often referred to as a “black box.” Once data are put into an algorithm, it’s not always known exactly how the algorithm arrives at its prediction. This can be particularly frustrating when things go wrong. A new mechanical engineering (MechE) course at MIT teaches students how to tackle the “black box” problem, through a combination of data science and physics-based engineering.

In class 2.C01 (Physical Systems Modeling and Design Using Machine Learning), Professor George Barbastathis demonstrates how mechanical engineers can use their unique knowledge of physical systems to keep algorithms in check and develop more accurate predictions.

“I wanted to take 2.C01 because machine-learning models are usually a “black box,” but this class taught us how to construct a system model that is informed by physics so we can peek inside,” explains Crystal Owens, a mechanical engineering graduate student who took the course in spring 2021.

As chair of the Committee on the Strategic Integration of Data Science into Mechanical Engineering, Barbastathis has had many conversations with mechanical engineering students, researchers, and faculty to better understand the challenges and successes they’ve had using machine learning in their work.

“One comment we heard frequently was that these colleagues can see the value of data science methods for problems they are facing in their mechanical engineering-centric research; yet they are lacking the tools to make the most out of it,” says Barbastathis. “Mechanical, civil, electrical, and other types of engineers want a fundamental understanding of data principles without having to convert themselves to being full-time data scientists or AI researchers.”

Additionally, as mechanical engineering students move on from MIT to their careers, many will need to manage data scientists on their teams someday. Barbastathis hopes to set these students up for success with class 2.C01.

Bridging MechE and the MIT Schwarzman College of Computing

Class 2.C01 is part of the MIT Schwarzman College of Computing’s Common Ground for Computing Education. The goal of these classes is to connect computer science and artificial intelligence with other disciplines, for example, connecting data science with physics-based disciplines like mechanical engineering. Students take the course alongside 6.C01 (Modeling with Machine Learning: from Algorithms to Applications), taught by professors of electrical engineering and computer science Regina Barzilay and Tommi Jaakkola.

The two classes are taught concurrently during the semester, exposing students to both fundamentals in machine learning and domain-specific applications in mechanical engineering.

In 2.C01, Barbastathis highlights how complementary physics-based engineering and data science are. Physical laws present a number of ambiguities and unknowns, ranging from temperature and humidity to electromagnetic forces. Data science can be used to predict these physical phenomena. Meanwhile, having an understanding of physical systems helps ensure the resulting output of an algorithm is accurate and explainable.

“What’s needed is a deeper combined understanding of the associated physical phenomena and the principles of data science, machine learning in particular, to close the gap,” adds Barbastathis. “By combining data with physical principles, the new revolution in physics-based engineering is relatively immune to the “black box” problem facing other types of machine learning.”

Equipped with a working knowledge of machine-learning topics covered in class 6.C402 and a deeper understanding of how to pair data science with physics, students are charged with developing a final project that solves for an actual physical system.

Developing solutions for real-world physical systems

For their final project, students in 2.C01 are asked to identify a real-world problem that requires data science to address the ambiguity inherent in physical systems. After obtaining all relevant data, students are asked to select a machine-learning method, implement their chosen solution, and present and critique the results.

Topics this past semester ranged from weather forecasting to the flow of gas in combustion engines, with two student teams drawing inspiration from the ongoing Covid-19 pandemic.

Owens and her teammates, fellow graduate students Arun Krishnadas and Joshua David John Rathinaraj, set out to develop a model for the Covid-19 vaccine rollout.

“We developed a method of combining a neural network with a susceptible-infected-recovered (SIR) epidemiological model to create a physics-informed prediction system for the spread of Covid-19 after vaccinations started,” explains Owens.

The team accounted for various unknowns including population mobility, weather, and political climate. This combined approach resulted in a prediction of Covid-19’s spread during the vaccine rollout that was more reliable than using either the SIR model or a neural network alone.

Another team, including graduate student Yiwen Hu, developed a model to predict mutation rates in Covid-19, a topic that became all too pertinent as the delta variant began its global spread.

“We used machine learning to predict the time-series-based mutation rate of Covid-19, and then incorporated that as an independent parameter into the prediction of pandemic dynamics to see if it could help us better predict the trend of the Covid-19 pandemic,” says Hu.

Hu, who had previously conducted research into how vibrations on coronavirus protein spikes affect infection rates, hopes to apply the physics-based machine-learning approaches she learned in 2.C01 to her research on de novo protein design.

Whatever the physical system students addressed in their final projects, Barbastathis was careful to stress one unifying goal: the need to assess ethical implications in data science. While more traditional computing methods like face or voice recognition have proven to be rife with ethical issues, there is an opportunity to combine physical systems with machine learning in a fair, ethical way.

“We must ensure that collection and use of data are carried out equitably and inclusively, respecting the diversity in our society and avoiding well-known problems that computer scientists in the past have run into,” says Barbastathis.

Barbastathis hopes that by encouraging mechanical engineering students to be both ethics-literate and well-versed in data science, they can move on to develop reliable, ethically sound solutions and predictions for physical-based engineering challenges.

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A new-age degree for a future-ready career: M.Tech In Data Science & Machine Learning from PES University

Data and AI will add USD 500 billion to India’s GDP by 2025, as per a report by McKinsey & Co and NASSCOM. As a result, the demand for quality data scientists who can interpret data to make informed decisions is all set to explode.

For professionals looking to use this demand to build a rewarding career, PES University’s M.Tech Program in Data Science and Machine Learning in collaboration with Great Learning imparts the industry-relevant and in-demand skills needed to break into the exciting world of data science.

Leveraging the latest data science tools and techniques is absolutely critical to structure business problems in machine learning and data science frameworks. With project-driven learning and mentorship from data science experts, learners will gain the skills and confidence they need to land rewarding jobs in data science, business analysis, ML engineering, and big data engineering roles.
Build real-world data science skills from the best

Learners will earn an M.Tech degree from a leading university to get a world-class learning experience and the credibility to stand out to recruiters.
PES University tops the list of New Universities Under 5 Years in Karnataka,
Secured the sixth position in ICARE India Private University Rankings 2019
Placed seventh on the list of India’s Most Trusted Educational Institutes
Comprehensive & industry-relevant curriculum

In this 2-year degree program with weekend classes at PES University, Electronic City campus in Bangalore, data science and machine learning concepts will be covered in four modules in the first year, followed by an M.Tech thesis and an intensive capstone project in the second.
Module 1

The first module in this program will familiarise candidates with Python for Data Science, SQL Database, and Statistical Methods for Decision Making.
Module 2

The second module will introduce Machine Learning Regression algorithms and eventually build up essential mathematical concepts and data visualisation.
Module 3

The third module will introduce Machine Learning Classification and Unsupervised algorithms, Time Series, and Natural Language Processing.
Module 4

The last module covers Deep Learning and Big Data. Areas such as neural networks, TensorFlow, Keras, Computer Vision, CNN and Applications, etc., will be the focus of this module.
Dedicated career support to realise your career dreams

Access career support with Great Learning Excelerate, with 1200+ hiring partners, has seen over 20,000 job vacancies and over 20,000 learners interviewed per month. Great Learning alumni work in top companies such as Google, Microsoft, Amazon, Netflix, Citibank, Uber, Adobe, Flipkart, and more.
Showcase your expertise to potential recruiters with an e-portfolio, a snapshot of the projects and skills acquired in the program, which is shareable across social media channels.
Resume building and interview preparation to improve your CV to highlight skills and previous professional experience. Crack interviews with interview preparation sessions.
Get career mentoring and guidance from industry experts who have transitioned to roles in the industry. Benefit from their guidance on how to build a rewarding career.
Access networking and program support with a dedicated program manager to solve queries. Interact with peers to grow your professional network.
How to apply?

To apply for this program, graduation in BE/B Tech/M Sc (Mathematics)/MCA from a recognised university with at least 50% in the undergraduate degree is needed. Candidates also need to have work experience of a minimum of 24 months by the time of the commencement of the program.

Power ahead in your career in the exciting field of data science with PES University’s M.Tech In Data Science & Machine Learning.

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Making smarter decisions with data analytics

High-value data and analytics can eliminate the guesswork in healthcare decision-making. Healthcare organizations with strong governance and good data analytics capabilities are better positioned to adapt to value-based care models.

The healthcare industry was starting to recognize the importance of high value data as they moved from fee-for-service reimbursement to value-based care models – but the COVID-19 pandemic made it even more apparent that hospitals need the ability to access and integrate data from multiple sources in order to make meaningful clinical and business decisions. TJ Elbert, Senior Vice President and General Manager of Data at Health Catalyst, said provider organizations, especially, were desperate to access trusted data as they treated patients in a virtual setting.

“There’s a real urgency when you are reliant on monitors and devices to get a full picture of a patient,” he pointed out. “That has increased the need for governance of all that data, from both inside and outside of the hospital, so providers feel like they can trust and then use that data to make decisions that impact care.”

Healthcare organizations with good data analytics capabilities in place, with strong governance, had a much easier time transferring to new models of care as they sought new ways to treat patients outside of the office setting, according to Elbert. In addition, they were also in a better position to make critical business decisions as the pandemic altered what kind of services they were allowed to provide.

“If you are able to use your data, you can better inform virtual care – and pivot to a more digital care model,” he said. “But more than that, you can make smarter decisions across the entire organization. Because when, all of a sudden, elective and outpatient surgeries are gone and you lose that revenue, you need to find a way to come back. Organizations with trusted data could figure out when it was safe to bring those services back, what capacity was needed to be for COVID-19, how to manage patient access to care and how to recover even as things kept changing. Having that information gives you a much fuller picture of what’s happening today – and where you need to be in the future.”

High-value data and analytics, truly, can eliminate the guesswork in healthcare decision-making, said Elbert. Unfortunately, he stated, many organizations still lack the tools to leverage the data they need, whether they are trying to facilitate team-based medicine and care collaboration or understand how to find the money to invest in new surgical technologies that will bolster their bottom line in the future.

“These capabilities can provide you the data to inform you where, when and how to shift – and how to do so in a way that puts your organization where it needs to be from a financial perspective so you are in a position to deliver the care your patients need,” said Elbert. “When you can do this modeling, you can tug on different threads and see what the impact will be, clinically and financially, to solve real problems. Data, really, is the key to allowing organizations to safely move forward with different initiatives – and, in doing so, improve patient outcomes and move the field, as a whole, forward.”

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