Neural networks

Part
01
of three
Part
01

In the past 10 years, what have been examples of innovative factories that are involved in mass manufacturing?

Hello there and thank you for your question regarding innovative factories who have boosted their mass manufacturing capabilities. The short answer is that Tesla in particular has great case studies and information covering their ability to meet increased production goals and a name associated with their efforts. Otherwise many companies have listed their capabilities but had no name attached to projects implemented for increasing manufacturing. Below you'll find a summary of the examples provided.

METHODOLOGY
Looking into industry trends, selective google searches, and company reports from large manufacturers was done with a focus on US brands initially. Unfortunately the research or publications involved in covering specifics or proof of increased production usually came from brands outside as well as inside the US in order to make a comprehensive list. In only one case was I able to find a company who had a name linked to their success. Even so, the creative involved was a veteran of the industry.

FUJITSU
Increase in labor costs, strong yen, and rigid regulations have been squeezing out the Japanese manufacturing scene in recent years. But when it comes to manufacturing however Fujitsu has been keen to keep up with automation and robotics to help smooth out the assembly line. However in recent years Fujitsu partnered with Intel to create a set of IoT solutions to remain competitive. The particular innovation comes with a repair and diagnostic center to automate detection of problems with manufactured goods. Tracking for defects has also allowed employees to monitor and account for goods, as well as "reduced the number of additional shipping trucks required due to delays, leading to a 30% cut in shipping costs."

OMRON
A partnership with Microsoft and Fujitsu allowed the company to map out and identify areas of their printed circuit board production that could see improvement. By harnessing the collection of data points, as well as informing findings to staff of how to utilize the data being collected there was a huge improvement in production. Non-experienced staff gained easier access and understanding to problem points in production- down the the singular unit. A decrease in the amount of people needed to contact and consult during a problem, "now one worker can analyze the root cause of the complex issues, which previously required six or more experienced people onsite to analyze". After implementing their big data strategy, hourly production was up by 30% in a matter of months.

TESLA
Big jumps in demand have pushed Tesla to constantly upgrade their factory floors over the years. Their main U.S. based production facility was an old Toyota factory which was sold in 2010. The company has executed two upgrades to the manufacturing floor in 2012 and 2014. The company largely opted for automated robots to " lift and maneuver entire cars with optimum precision while taking up less room". Conveyors, robots, working docks, and improved facilities for employees have continuously increased production speed while allowing the company to keep up with increasing waves of demand as new models come to fruition. While not aimed at explaining production innovations, this interesting look at the finances and decisions behind Tesla's success can be read here.

In this particular case, a name was associated with the company's program. Tesla had hired Audi's previous Director of Production Peter Hochholdinger.

QBOTIX
Solar power has made a slow but steady climb towards the commercial market. But by utilizing robots over labor for installation, companies can now afford to install and utilize large solar powered plants over labor intensive installations of the past. The innovations by the German based company supplying these robots aren't the only innovation however in helping support larger productions of solar panels. Startup QBotix has also unleashed a robot that has eliminated the amount of steel needed during production for solar panels by introducing a cheaper robot that can help solar panels tilt and catch the best of the sun rays available.

SIEMENS
Lithium-ion battery production saw a huge kick when German conglomerate Siemens invested in automating its testing plant. Innovations in an additional report noted that automation had led to fewer rejects, more precision and consistency in battery coating, as well as "safe integration of quality measuring systems".

CONCLUSION
To wrap it up, Fujitsu, Omron, Telsa, QBotix, and Siemens have increased capacity and production by investing smartly in automated processes as well as thinking creatively about the essentials of their products themselves.

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Part
02
of three
Part
02

Who are 10 of the top winners from the 2016 ImageNet competition?

Hello! Thanks for your question about the winners of the 2016 ImageNet competition. The short answer is that unfortunately I was unable to identify any winners that are currently based in the United States; however, I was able to provide you with the requested details for 10 of the winners.

Below you will find a deep dive of my findings.

FINDINGS
To answer this question I identified the winner teams of each category based on the ImageNet results website. I then compiled a list of the members of each individual winning team (see tabs on this spreadsheet for each team name).

With each individual teammember, I conducted a search of LinkedIn using their name and the term "ImageNet". This turned up a number of entries but did not provide me with the full requested 10. I subsequently started going through each name on the list searching for the individual's name and the name of the team they were a member of. This approach allowed me to fill out the remaining entries in the "Individuals" tab of the spreadsheet.

Unfortunately, after going through all the members of the winning teams I was unable to identify any that are currently located in the United States, so I went ahead and included all of them that I could find (including their location).

CONCLUSION
To wrap it up, while many winners of the 2016 ImageNet contest do not currently have LinkedIn accounts, I was able to find 10 that did. For each of these I provided their location, LinkedIn account, and their education (when available).

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Part
03
of three
Part
03

Who are 10 winners of neural network or computer vision competitions from the last 2 years in North America?

Hello! Thanks for your question about North American winners of neural network or computer vision competitions. The short version is that after searching extensively through academic databases, corporate websites, and industry reports, I’ve determined that the information you requested is not publicly available because the majority of the winners of well-known competitions are from China and Europe. However, I was able to learn of six people who are from North America who won well-known neural network/computer vision competitions, excluding ImageNet. Below you will find a deep dive of my research and methodology.

METHODOLOGY
I searched through academic databases, corporate websites, and industry reports looking for competitions or challenges pertaining to neural networks, deep learning, or computer vision. I discarded any results that were older than 2015 or did not take place in North America. From this list, I discarded any competition that wouldn't be considered well known.

Because there is no set standard for when a competition is well known or not, I had to come up with criteria by which I could determine if a particular competition was well known. I looked at the origins of the teams competing and reasoned that any competition that only had contestants from one area wouldn't be well-known. Any competition or challenge that had contestants from all over the world would probably be considered well-known.

Given the criteria for a well-known competition or challenge, I disregarded any competition that did not have contestants from around the world.

From this list, I searched for winners from 2015, 2016, and 2017 competitions/challenges. I disregarded any winner who was not from or residing in North America. Additionally, I discounted any winners who participated in the ImageNet competition.

This left me with six names. I verified their status as residents in North America by locating their LinkedIn profiles. I then searched their University websites for information on their education. I summarized my findings below.

HELPFUL FINDINGS
While I could not find a direct answer to your question, I was able to gather some information about this topic, which I think will be helpful for your project. The two competitions that had North American winners were the International Neural Network Society's Awards Program and the LDV Vision Summit Competition.

The International Neural Network Society Awards Program recognizes people who have significantly contributed to the field of neural networks. These awards fall into four categories, the Hebb, Helmholtz, Gabor, and Young Investigator Awards. The awards recognize achievements for biological learning, sensation/perception, engineering/application, and newcomers who have significantly contributed to the field respectively.

The LDV Capital Vision Summit Competitions consist of two separate events. The first is the Startup Competition, which seeks new businesses that inject innovation into the industry through applications of neural networks. The second is the Entrepreneurial Computer Vision Challenge, which is awarded to individuals who solve a problem in the industry through the novel application of neural networks.

1. Donald Wunsch - 2015 INNS Gabor Award. Dr. Wunsch received his Ph.D. in Electrical Engineering from the University of Washington in Seattle in 1991. He went on to get an EMBA in Business Administration at Washington University in St. Louis in 2006. He is currently a professor of Computer Engineering at Missouri University of Science and Technology. Between teaching, he studies clustering, reinforcement learning, and other neural network applications.

2. Kyriakos Vamvoudakis - 2016 INNS Young Investigator Award. Dr. Vamoudakis is currently an assistant professor of engineering at Virginia Tech. His research includes control theory, game theory, renewable energy, and computation intelligence. He received his Ph.D. in 2011 from the University of Texas at Arlington.

3. Hava Siegelmann - 2016 INNS Hebb Award. Dr. Siegelman is currently a professor of Computer Science at the University of Massachusetts. Her research focuses on modeling neural networks, intelligence vis-a-vis adaptive intelligence, cognitive models, and robotic interface. She received her Ph.D. from Rutgers University in 1993.
4. Jameson Detweiler - 2017 LDV Capital Vision Summit Startup Competition. Jameson Detweiler. He attended Drexel University as a Ph.D. student studying Civil, Architectural, and Environmental Engineering. At Drexel University he led the team working on the Drexel SmartHouse initiative.

5. Timnit Gebru - 2017 LDV Capital Vision Summit Entrepreneurial Computer Vision Challenge. She is currently a Ph.D. candidate at Stanford. She's studying large datasets and data-processing. She hopes to utilize this information to improve image recognition systems.

6. Sean Bell - 2016 LDV Vision Summit Entrepreneurial Computer Vision Challenge. Dr. Bell received a Ph.D. from Cornell University in 2016. His thesis was on using recurrent neural networks and skip pooling protocols to detect objects in context.

POSSIBLE FUTURE RESEARCH
If the search criteria were to include international winners, I would be able to provide a much larger list of winners. Using LinkedIn, this list could be restricted to winners who spoke English. I found a team of researchers employed by Microsoft who won a prestigious industry award, but because they were located in China I couldn't include them in the list.

Additionally, ImageNet appears to be the largest and best known neural network/computer vision competition in the world. I was able to locate five individuals from North America who won this competition. If the search was broadened to include ImageNet I could provide an expanded list of names and profiles.

CONCLUSION
To wrap it up, after searching extensively through academic databases, corporate websites, and industry reports, a direct answer to your question is not publicly available because the majority of the winners of well-known competitions are from China and Europe. However, I learned that Hava Siegelmann, Kyriakos Vamvoudakis, and Donald C. Wunsch II have all won International Neural Network Society contests.Jameson Detweiler, Timnit Gebru, and Sean Bell have all won LDV Capital computer vision computer competitions. If you’d like to continue research on any of the points outlined above, just let us know!

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