David Lindell, a graduate student in electrical engineering at Stanford University, along with his team, developed a camera that can watch moving objects around corners. When they tested the new technology, Lindell wore a high visibility tracksuit as he moved around an empty room. They had a camera that was aimed at a blank wall away from Lindell, and the team was able to watch all of his movements with the use of a high powered laser. The laser reconstructed the images through the use of single particles of light that were reflected onto the walls around Lindell. The newly developed camera used advanced sensors and a processing algorithm.
Gordon Wetzstein, assistant professor of electrical engineering at Stanford, spoke about the newly developed technology.
“People talk about building a camera that can see as well as humans for applications such as autonomous cats and robots, but we want to build systems that go well beyond that,” he said. “We want to see things in 3D, around corners and beyond the visible light spectrum.”
The camera system that was tested will be presented at the SIGGRAPH 2019 conference on August 1.
The team has already developed similar around-the-corner cameras in the past, but this one is able to capture more light from more surfaces. It can also see wider and farther as well as monitor out-of-sight movement. They are hoping that these “superhuman vision systems” will be able to be used in autonomous cars and robots so that they will operate more safely than when controlled by a human.
One of the team’s main goals is to keep the system practical. They use hardware, scanning and image processing speeds, and styles of imaging that are already used in autonomous car vision systems. One difference is that the new system is able to capture light bouncing off of a variety of different surfaces with different textures. Before, the systems that were used to see things outside of a camera’s line of sight were only able to do so with objects that reflected even and strong light.
One of the developments that helped them create this technology was a laser that is 10,000 times more powerful than the one they used last year. It scans a wall on the opposite side of the point of interest. The light bounces off the wall, hits the objects in the scene, and returns back to the wall and camera sensors. The sensor is then able to pick up small specks of the laser light and sends them to an algorithm that was also developed by the team. The algorithm deciphers the specks to reconstruct the images.
“When you’re watching the laser scanning it out, you don’t see anything,” Lindell said. “With this hardware, we can basically slow down time and reveal these tracks of light. It almost looks like magic.”
The new system is able to scan at four frames per second and reconstruct scenes up to 60 frames per second with a computer graphics processing unit that enhance the capabilities.
The teams drew inspiration from other fields such as seismic imaging systems. Those bounce soundwaves off underground layers of Earth, and they are able to see what’s beneath the surface. The algorithm is reconfigured to decipher light that bounces off of hidden objects.
Matthew O’Toole, assistant professor at Carnegie Mellon University and previous postdoctoral fellow in Wetzstein’s lab, spoke about the new technology.
“There are many ideas being used in other spaces — seismology, imaging with satellites, synthetic aperture radar — that are applicable to looking around corners,” he said. We’re trying to take a little bit from these fields and we’ll hopefully be able to give something back to them at some point.”
The team’s next step is testing the system on autonomous research cars. They also want to see if it will be applicable in other areas such as medical imaging and to help combat problems of visual conditions that drivers encounter such as fog, rain, sandstorms, and snow.
Chadwick Xu, Co-Founder & CEO of Shenzhen Valley Ventures – Interview Series
Chadwick Xu, is the Co-Founder & CEO of Shenzhen Valley Ventures, a hardware-focused company for engineers, by engineers. They partner with startups to prototype, design for manufacturing, test, debug, and manufacture their hardware products.
When did you first want to become an entrepreneur?
From day one when I first came to Shenzhen in 1992, and this is the very reason I came to the city. The path I followed is similar to many other young graduates (at that time), working for a corporate to learn, accumulate expertise, and build connections, then they start their own business.
You founded Zowee Technology in 2004 which was officially listed on the Shenzhen Stock Exchange in 2010. Could you give us some details on what Zowee is and the journey you had creating such a successful company?
The founders of Zowee knew each other by doing business, each partner had their own business before Zowee was founded. The idea is that, instead of having many small businesses, it made more sense to combine the small businesses together and build a bigger company, hence Zowee was born.
Zowee was positioned as a contract manufacturer, and it has remained to be so till today. Besides the effort we put into it, I think the major driving power of its success is the overall economic environment. The 1990s and 2000s was the golden era for Chinese to enter into private businesses. China’s opening up policy and the strong global demand created an ideal opportunity.
Shenzhen Valley Ventures (SVV) is your newest venture. What was the inspiration behind this business?
The success of Zowee represents the opportunity of “Made in China”, but in the future, the big opportunity is not in manufacturing, it’s in technology. What Zowee took advantage of was the labor bonus China had, and what SVV is trying to take advantage of is the engineering bonus China now has today. China is still falling behind on research, but China has the most engineering talent (globally). If SVV can put together a mechanism, enabling the best engineering resources to support the advanced research, so that research could be commercialized faster, it will be a mutually beneficial business structure.
Furthermore, China is becoming the largest single market, both in B2C and B2B business, if SVV’s engineering platform could help international deep-tech startups enter the Chinese market more efficiently, that will be another bonus.
What are some of the more interesting companies that are using SVV’s batch manufacturing services?
There are three groups of projects that have been using SVV’s small-batch manufacturing services:
- Startups: Generally, there is a long growing curve for startup companies to reach the volume that a contract manufacturer is willing to take. SVV has been helping startups when they are still very small and in the early stages of their delivery. This group is the group SVV has been supporting the most. e.g., Neosensory Buzz, a wearable device maker that helps people suffering from hearing or visual problems can now “hear” environmental noises with their skin, and eventually improve their living quality. Sutro, a water quality monitoring device that allows users to access their pool water quality data 24/7, streaming wireless to their cellphone.
- Corporate innovation projects: These projects are identified as “testing the water” projects in a corporate, it may or may not convert into a commercial project, depending on how the testing goes. e.g., an IoT box B/S/H tested to convert non-connected home appliances into connected smart appliances, or the air quality data monitoring device MANN+HUMMEL plans to embed into their existing air filters.
- Universities and institutional research projects: Some researchers need to have physical devices built and deployed to validate or prove the research they made, e.g., an earthquake forecast system/algorithm Beijing university have been researching, SVV helped them to design and build several hundreds of units, these testing units have been deployed nation wide in China, helping researchers collect huge amounts of valuable data for their studies. From 2019, international institutes joined in the study program, and more units are being deployed in Taiwan, Japan, Pakistan, India, and other countries.
SVV enables companies to use their services to prototype products before full scale production launches. Could you elaborate on why SVV is a good partner for prototyping versus other competitors?
SVV is not only a prototyping service provider, instead, it’s a full-cycle development platform provider, including prototyping, engineering, development, testing, certification pre-scanning, pilot production validation, and commercial-ready small-scale production.
This full cycle platform is usually owned by large-scale manufacturers, for the business of large-scale orders from large brands. A innovation dedicated full-cycle platform is a scarce resource, not only for startups, but also for corporations “testing the water” innovation projects.
Can you discuss how SVV is using computer vision to guide manufacturing robots towards better assembly?
SVV is not deploying computer vision guided robotics on its own production lines since SVV’s facilities are used only for small-scale production and for less mature products, the products of such products are more efficient with using manual assembly, since robots are more efficient in producing higher volume and mature designed products. However, SVV has been supporting computer vision guided robotic startups throughout their development.
At a recent Web Summit panel, you discussed how robots will soon be taking over manufacturing from humans, due to how precise robots are. How long until humans are completely removed from the assembly process?
It’s difficult to predict a precise point in time, but it may come much faster than most people think, and it will come gradually, the more standardized the assembly process becomes, the earlier it will be replaced by robots, and gradually will get to the level of fully automated processes.
Just as shown in “American Factory”, there will be a longer period to have robots and humans working together, with the trend of more and more humans replaced by robots, if you stay in a factory for a long period, this replacement process is quite visible.
What are your views on the current AI rivalry between the United States and China?
Competition is always good for the economy and technology, and in almost every competition scenario, the output is not like boxing, with the end result as a lose or win, instead, it often ends up as a symbiotic relationship, each party ends up with its own differentiated advantage.
The past several decades has been the history that China gradually catches up from low-end to middle-end innovation, in areas such as, telecommunications, to directly compete with the US, but the US still takes the leading position in most areas. In the 1800’ and 1900’, US and EU worked very hard, just like the Chinese are doing now, but the wealth and technology advantage somehow created a lay back, welfare dependent society. In many cases, China’s catching up serves as a power to push the US and EU out of their comfort zones and drive them to be more innovative to keep the leading position.
Is there anything else that you would like to share about SVV?
I have been reiterating my opinion on jobs being taken by robots, most discussions are highlighting the bad side, taking jobs from people, increasing the joblessness percentage, creating severe social problems. But on the good side, imagine if smarter machines were built, to improve production efficiency unlimitedly high, constantly growing until the machines can produce an abundance of materials for everyone on this planet, that is supposed to be the bright ending for the AI revolution.
This is almost exactly what Karl Marx describes as the ideal world, “From each according to his ability, to each according to his need”. Or, a perfect utopian society. For the first time in human history we have the opportunity to witness the elimination of hunger entirely from Earth.
We are excited that SVV is a part of this new AI revolution, to help more creative ideas become reality. Drops of water can eventually become flowing rivers, the idea of participating and being witness to this new history is hugely rewarding for us.
Thank you for this great discussion on robotics. To learn more readers may visit Shenzhen Valley Ventures.
Sanchit Mullick, Assoc. Vice President for AI & Automation at Infosys – Interview Series
Sanchit Mullick is associate vice president and global head of Sales for AI and Automation Services at Infosys. In this role, he leads worldwide sales, marketing and alliances for AI and Automation Services and partners with customers to help them chart their roadmap across the Automation spectrum, leveraging everything from robotic automation to cognitive services. Mullick has worked across the U.S., Australia, the UK and India, having played roles spanning sales, consulting and deliver
Infosys enables enterprise companies to integrate robotic process automation (RPA). What are some of the reasons why companies should consider these options?
RPA enables businesses to unlock operational efficiencies in the form of capacity release, cost avoidance through flattening the work curve, higher quality through repeated robotic actions, improved employee experience through removal of mundane, boring tasks, and improved customer experience through consistent behavior and better average handling time.
In the early stages of adoption, RPA can play a pivotal role in uplifting organization productivity. However, as adoption spreads across the enterprise and with low code / no code platforms driving citizen development in some enterprises, we see RPA as an effective tool to uplift individual productivity.
What are some examples of RPA tasks Infosys can assist with?
Infosys can assist any business across any industry that has repeatable, rule-based routine tasks being performed by humans. Most RPA adoption programs start in enterprise functions such as Finance, Accounting, HR, Procurement amongst others. This is because these functions lend themselves to cross-industry portability of use cases and automation ideas. Increasingly, however, there are instances of RPA being used to drive efficiency and effectiveness in core business processes. For example, we are working with a large banking client of ours to reimagine their Treasury operations using automation and AI.
An early limitation of RPA platforms was that they could only work off structured data as the input. However, most RPA platforms have now evolved to the point where they are able to apply techniques to first convert semi- or unstructured data into structured data thus increasing the pipeline of opportunities for automation, and in doing so transitioning into Intelligent Process Automation.
Does Infosys also offer AI Automation training?
We have developed an always-on learning approach and have democratized learning through our internal training platform, Lex. We have created learning paths with different levels of certification that allow our employees to become “Automation Professionals” or “AI Professionals” or both. The content for these learning paths has been put together with inputs from our software product partners, as well as our academic partnerships.
We offer these training programs and extend this learning value chain to our clients as they seek to reskill / upskill their employees. We have also created micro capsules around specific RPA platforms which can be leveraged to drive quick enablement amongst business users, which in turn can facilitate effective ideation. This is extremely important in the automation and AI domain, because the first step to driving success in automation adoption is to identify the best use cases and in most cases (dare I say all) this comes through effective participation from business.
Infosys offers a ‘Technology Rethink,’ so that the fundamental changes that are needed in the application landscape and infrastructure can be assessed. Could you walk us through an example of how this would be applied?
Think of how the process is being done, how the process will change over time, and the way the process interacts with other units within the organization: these are the considerations we take before starting an RPA project. To rethink how we use technology ultimately helps us rethink how we do work. At Infosys, we strive to bring this philosophy with us every day, and apply it to how we collaborate with our clients. With the democratization of AI and RPA, organizations have the opportunity to take leaps and bounds with the frameworks we’ve developed to undertake their digital transformation initiatives.
A vast majority of traditional organizations are sitting on applications and infrastructure assets that are dated. Legacy Modernization is one of Infosys’ core offerings to help our clients renew and refresh their offerings to the market. Purely from an AI & Automation stand point, our process discovery approaches encourage business teams to rethink how they should refresh their business processes according to changing market dynamics. For example, our AI driven IT Operations approach brings deep insights from the application and infrastructure through analysis of service tickets, alerts and exceptions to help develop AI driven humanless service desks, self-heal and preventative maintenance approaches to reduce the manual effort across client’s IT operations.
Infosys enables the creation of a smart workforce with the Infosys conversational AI solution. Could you share with us some details regarding the natural language processing that is used by this chatbot, and why it is superior to competing solutions?
Our internally developed NIA chatbot solution was designed to help all business units within Infosys apply conversational AI into their departments, and with this experience we are now able to service our clients using the same solution. A few features such as our FAQ extractor, utterance creator, and easy-to-use admin panel help with fast chatbot deployment.
Additionally, our ability to integrate with communication platforms such as Facebook, Skype and WhatsApp allows our clients to interface with their customers and vendors in a unified way. Adding RPA with Conversational AI allows for action bots to complete tasks and common requests. Active learning can be applied to chatbots to allow for bots to get smarter over time with human intervention. We leverage open source NLP algorithms as well as proprietary offerings from Google, Azure, AWS and Watson that we have integrated into the Nia chatbot. This allows us to continually improve based on the progress made in the industry and also leverage any investments already made by our clients.
Is a decision tree always used with this chatbot, or is this on a case-by-case basis?
The decision tree is a fundamental component to help chatbots understand the workflow requests with the users it interacts with. Other techniques can be used to enhance the accuracy and user interaction of the bot.
What are some interesting new products or solutions that Infosys is working on?
Bot Factory, which is a repository of prebuilt ‘micro bots’ that allows users to stitch together prebuilt automations for rapid RPA deployment.
Then there’s MiniChat, which has been developed based on NLP algorithms for quick and correct response to the user. The solution needs a fixed set of FAQs to perform initial training and generating training data as it uses utterances given in training data for answering user queries. It reduces human effort to find a solution for frequently occurring problems / issues. MiniChat can be deployed on any webpage within a time span of 2-3 hours.
Finally, email work bench. Organizations across the globe deal with a myriad of unstructured emails from clients be it for buying a product, requesting a change in their details, or complaints and feedback. Infosys has developed an NLP-driven model-based solution to help extract usable information and automate an action based on the trigger.
These are only some examples of solutions that we constantly develop as part of every project that we execute and then make available to our clients worldwide to drive improved delivery.
Is there anything else that you would like to share about Infosys?
There has been an enormous amount of coverage around automation and AI. In all of this, it is important to remain focused on solving real world problems. And Infosys is addressing this by focusing on both problem finding and problem solving. We are driving this by creating an ecosystem that brings together consulting, technology and operations. Also by treating the entire spectrum of tools as a continuum. From the very beginning we have housed automation and AI capabilities under a single umbrella, and by doing so we bring various technology interventions together to solve a business problem in the best way possible.
Our work has also been recognized by industry leading analysts. Infosys has been rated as a Leader in IDC’s Intelligent Automation Services MarketScape 2019, in our first ever rating in this space. This rating is based on a comprehensive and rigorous framework evaluating IA players on criteria such as strategy, delivery, marketing and client satisfaction. The study highlights the factors expected to be the most influential for success in the market, both in the short and the long term.
HFS Research recently published a viewpoint on why Enterprise AI implementation initiatives should have a business-first approach where HFS called out Infosys’ well-known consulting capabilities and business outcome-focused approaches, our approach to assuming the role of a trusted advisor while deploying AI into a client’s environment, as well as our ability to maintain an end-to-end journey view with a value-creation paradigm.
To learn more visit Infosys
Artificial Intelligence Used to Prevent Icebergs from Disrupting Shipping
Experts at the University of Sheffield have developed a combination of control systems and artificial intelligence (AI) forecasting models to prevent icebergs from drifting into busy shipping regions.
Through the use of a recently published control systems model, experts were able to predict the movement of icebergs. In 2020, between 479 and 1,015 icebergs are expected to drift into waters south of 48°, an area that sees great shipping movement between Europe and north-east North America. Last year, there were a total of 1,515 observed in that same area.
The team relied on experimental artificial intelligence analysis in order to independently support the number of icebergs predicted. They also discovered a rapid early rise in the number of icebergs present in this area during the ice season, which runs from January to September.
The International Ice Patrol (IIP) is supplied with the findings, and they use the information to figure out the best use of resources for better ice forecasts during the season. According to the seasonal forecast, ships in the north-west Atlantic will be less likely to encounter an iceberg compared to last year.
Icebergs cause serious problems and shipping risks in the north-west Atlantic. Records show that there have been collisions and sinkings dating back to the 17th century. The IIP was established in 1912 after the sinking of the Titanic, and its job is to observe sea ice and conditions in the north-west Atlantic and warn of potential dangers.
The risk of icebergs to shipping changes each year. One year can see no icebergs crossing the area, while another year can see over 1,000. This makes it difficult to predict, but in general, there has been a higher amount detected since the 1980s.
2020 is the first year that artificial intelligence is being used to forecast the icebergs in the area, as well as the rate of change across the season.
The model was developed by a team led by Professor Grant Bigg at the University of Sheffield, and it was funded by insurance firm AXA XL’s Ocean Risk Scholarships Programme. There is a control systems model as well as two machine learning tools that are used.
Data related to the surface temperature of the Labrador Sea is analyzed, as well as variations in atmospheric pressure in the North Atlantic and the surface mass balance of the Greenland ice sheet.
The foundation control systems approach had an 80 percent accuracy level when tested against data on iceberg numbers for the seasons between 1997 and 2016.
According to some of Professor Bigg’s earlier research, the variation of the number of icebergs drifting into the region was due to variable calving rates from Greenland. However, the regional climate and ocean currents are the biggest factors. Higher numbers of icebergs appear when there are colder sea surface temperatures and stronger northwesterly winds.
Grant Brigg is a Professor of Earth System Science at the University of Sheffield.
“We have issued seasonal ice forecasts to the IIP since 2018, but this year is the first time we have combined the original control system model with two artificial intelligence approaches to specific aspects of the forecast. The agreement in all three approaches gives us the confidence to release the forecast for low iceberg numbers publicly this year—but it is worth remembering that this is just a forecast of iceberg conditions, not a guarantee, and that collisions between ships and icebergs do occur even in low ice years.”
According to Mike Hicks of the International Ice Patrol, “The availability of a reliable prediction is very important as we consider the balance between aerial and satellite reconnaissance methods.”
Dr. John Wardman is a Senior Science Specialist in the Science and Natural Perils team at AXA XL.
“The impact of sea level rise on coastal exposure and a potential increase in Arctic shipping activity will require a greater number and diversity of risk transfer solutions through the use of re/insurance products and other ‘soft’ mitigation strategies. The insurance industry is keeping a keen eye on the Arctic, and this model is an important tool in helping the industry identify how or when the melting Greenland Ice Sheet will directly impact the market.”
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