A serial entrepreneur, writer, and tech investor, Adam Benzion is the co-founder of Hackster.io, the world’s largest community for hardware developers.
Aluminum and iconography are no longer enough for a product to get noticed in the marketplace. Today, great products need to be useful and deliver an almost magical experience, something that becomes an extension of life. Tiny Machine Learning (TinyML) is the latest embedded software technology that moves hardware into that almost magical realm, where machines can automatically learn and grow through use, like a primitive human brain.
Until now building machine learning (ML) algorithms for hardware meant complex mathematical modes based on sample data, known as “training data,” in order to make predictions or decisions without being explicitly programmed to do so. And if this sounds complex and expensive to build, it is. On top of that, traditionally ML-related tasks were translated to the cloud, creating latency, consuming scarce power and putting machines at the mercy of connection speeds. Combined, these constraints made computing at the edge slower, more expensive and less predictable.
But thanks to recent advances, companies are turning to TinyML as the latest trend in building product intelligence. Arduino, the company best known for open-source hardware is making TinyML available for millions of developers. Together with Edge Impulse, they are turning the ubiquitous Arduino board into a powerful embedded ML platform, like the Arduino Nano 33 BLE Sense and other 32-bit boards. With this partnership you can run powerful learning models based on artificial neural networks (ANN) reaching and sampling tiny sensors along with low-powered microcontrollers.
Over the past year great strides were made in making deep learning models smaller, faster and runnable on embedded hardware through projects like TensorFlow Lite for Microcontrollers, uTensor and Arm’s CMSIS-NN. But building a quality dataset, extracting the right features, training and deploying these models is still complicated. TinyML was the missing link between edge hardware and device intelligence now coming to fruition.
On the same day that Elon Musk defied local regulations and reopened Tesla’s factory in Fremont, California, the CEO put a new person in charge of production.
Musk named Richard Miller, who was director of paint operations at Tesla, to head of production at the factory, according to an internal email sent to employees Monday and viewed by TechCrunch. It appears that Miller replaces Jatinder Dhillon, who was the company’s manufacturing director. CNBC reported in March that Dhillon had left the company, although his LinkedIn profile still shows he is at the company and in the same role.
An email has been sent to Musk and Tesla for comment.
“Due to excellent performance as head of paint operations in Fremont, Richard Miller is hereby promoted to overall head of Fremont Production. Congratulations!,” the email reads.
The promotion comes at a chaotic moment for Musk and Tesla. Production at the company’s Fremont factory — where its electric vehicles are assembled — has been suspended since March 23 due to stay-at-home orders issued by Alameda County and Gov. Gavin Newsom. Musk restarted production Monday in direct conflict with county orders.
Tesla had planned to bring back about 30% of its factory workers May 8 as part of its reopening plan, after Newsom issued new guidance that would allow manufacturers to resume operations. However, the governor’s guidance included a warning that local governments could keep more restrictive rules in place. Alameda County, along with several other Bay Area counties and cities, have extended the stay-at-home orders through the end of May. The orders were revised and did ease some of the restrictions. However, it did not lift the order for manufacturing.
Musk has been at war with Alameda County, specifically aiming his ire at health officials, ever since the order was extended. Over the weekend, he threatened to sue and pull operations out of California. Tesla filed a lawsuit later that day against Alameda County seeking injunctive relief.
On Monday, Musk escalated matters further and announced on Twitter that he had restarted production.
Musk wrote he would “be on the line,” a reference to the assembly line at the factory where Tesla makes the Model X, Model S, Model 3 and Model Y. He added “if anyone is arrested, I ask that it only be me.”
Alameda County issued a statement Monday acknowledging that it had learned that the Tesla factory in had opened beyond “minimum basic operations,” which was allowed.
“We have notified Tesla that they can only maintain Minimum Basic Operations until we have an approved plan that can be implemented in accordance with the local public health order,” the statement sent to TechCrunch said. “We are addressing this matter using the same phased approach we use for other businesses which have violated the order in the past, and we hope that Tesla will likewise comply without further enforcement measures.”
The county added that since April 30 it has “continued to collaborate in good faith with Tesla to present a plan for reopening the Fremont plant that ensures the safety of their thousands of employees and the communities in which they live and work, and that also aligns with local and state requirements.”
“We continue to move closer to an agreed upon safety plan for reopening beyond Minimum Basic Operations by working through steps that Tesla has agreed to adopt,” the statement continued. “These steps include improving employee health screening procedures and engaging front-line staff on their concerns and feedback regarding safety protocols.”
The county said it expected Tesla to submit a site-specific plan later Monday as required under the State of California guidance and checklist for manufacturing issued on May 7.
Tesla filed a lawsuit Saturday against Alameda County in an effort to invalidate orders that have prevented the automaker from reopening its factory in Fremont, California.
The lawsuit, which seeks injunctive and declaratory relief against Alameda County, was first reported by CNBC. The lawsuit was filed in U.S. District Court for California’s Northern District.
Earlier Saturday, Tesla CEO Elon Musk tweeted that he was filing a lawsuit against Alameda County and threatened to move its headquarters and future programs to Texas or Nevada immediately.
Tesla had planned to bring back about 30% of its factory workers Friday as part of its reopening plan, defying Alameda County’s stay-at-home order. Musk was basing the reopening on new guidance issued Thursday by California Gov. Gavin Newsom that allows manufacturers to resume operations. The guidance won praise from Musk, who later sent an internal email to employees about plans to reopen based on the governor’s revised order. However, the governor’s guidance included a warning that local governments could keep more restrictive rules in place. Alameda County, along with several other Bay Area counties and cities, last week extended the stay-at-home orders through the end of May. The orders were revised and did ease some of the restrictions. However, it did not lift the order for manufacturing.
The lawsuit argues that by preventing Tesla from opening, the Alameda County is going against its own guidance.
“Alameda County has expressly recognized and publicized that “businesses may . . . operate to manufacture” batteries and electric vehicles,” the complaint reads. “Inexplicably, however, the Third Order as well as County officials have simultaneously insisted that Tesla must remain shuttered, thereby further compounding the ambiguity, confusion and irrationality surrounding Alameda County’s position as to whether Tesla may resume manufacturing activities at its Fremont Factory and elsewhere in the County.”
The term “third order” is a reference to a revised stay-in-place order issued by Alameda County.
On Friday, the Alameda County Health Department said Tesla had not been given “the green light” to reopen and said if the company did, it would be out of compliance with the order.
Tesla CEO Elon Musk said Saturday the company will file a lawsuit against Alameda County and threatened to move its headquarters and future programs to Texas or Nevada immediately, escalating a fight between the company and health officials over whether its factory in Fremont can reopen.
Tesla had planned to bring back about 30% of its factory workers Friday as part of its reopening plan, defying Alameda County’s stay-at-home order.
TechCrunch has reached out to Elon Musk directly. We will update the story if he responds.
California Gov. Gavin Newsom issued new guidance Thursday that allowed manufacturers to resume operations. The guidance won praise from Musk, who later sent an internal email to employees about plans to reopen based on the governor’s revised order. However, the governor’s guidance included a warning that local governments could keep more restrictive rules in place. Alameda County, along with several other Bay Area counties and cities, last week extended the stay-at-home orders through the end of May. The orders were revised and did ease some of the restrictions. However, it did not lift the order for manufacturing.
On Friday, the Alameda County Health Department said Tesla had not been given “the green light” to reopen and said if the company did, it would be out of compliance with the order.
In the tweet, Musk said Tesla is filing a lawsuit against Alameda County immediately. In a later tweet, he also encouraged shareholders to file a lawsuit against the county.
“The unelected & ignorant “Interim Health Officer” of Alameda is acting contrary to the Governor, the President and our Constitutional freedoms & just plain common sense!,” the tweet said. He followed up with another tweet claiming that Tesla will now move its HQ and future programs to Texas or Nevada immediately.
“If we even retain Fremont manufacturing activity at all, it will be dependent on how Tesla is treated in the future. Tesla is the last carmaker left in CA,” Musk wrote.
Frankly, this is the final straw. Tesla will now move its HQ and future programs to Texas/Nevada immediately. If we even retain Fremont manufacturing activity at all, it will be dependen on how Tesla is treated in the future. Tesla is the last carmaker left in CA.
Tesla has operations in Nevada; it doesn’t in Texas. The company’s massive battery factory – known as Gigafactory 1 — is located in Sparks, Nevada. Tesla is seeking out a new location to build a new U.S. gigafactory that will produce the Cybertruck and Model Y crossover. Some have speculated that Texas is a top pick.
Sources have told TechCrunch that Tesla is in talks with Nashville officials to locate a factory there that will produce the Cybertruck and Model Y crossover.
“Scouting locations for Cybertruck Gigafactory. Will be central USA,” Musk tweeted in March. He added that the factory would be used to produce Model Y crossovers for the East Coast market. The first Model Y vehicles are being produced at its plant in Fremont.
“Assembly” may sound like one of the simpler tests in the manufacturing process, but as anyone who’s ever put together a piece of flat-pack furniture knows, it can be surprisingly (and frustratingly) complex. Invisible AI is a startup that aims to monitor people doing assembly tasks using computer vision, helping maintain safety and efficiency — without succumbing to the obvious all-seeing-eye pitfalls. A $3.6 million seed round ought to help get them going.
The company makes self-contained camera-computer units that run highly optimized computer vision algorithms to track the movements of the people they see. By comparing those movements with a set of canonical ones (someone performing the task correctly), the system can watch for mistakes or identify other problems in the workflow — missing parts, injuries and so on.
Obviously, right at the outset, this sounds like the kind of thing that results in a pitiless computer overseer that punishes workers every time they fall below an artificial and constantly rising standard — and Amazon has probably already patented that. But co-founder and CEO Eric Danziger was eager to explain that this isn’t the idea at all.
“The most important parts of this product are for the operators themselves. This is skilled labor, and they have a lot of pride in their work,” he said. “They’re the ones in the trenches doing the work, and catching and correcting mistakes is a big part of it.”
“These assembly jobs are pretty athletic and fast-paced. You have to remember the 15 steps you have to do, then move on to the next one, and that might be a totally different variation. The challenge is keeping all that in your head,” he continued. “The goal is to be a part of that loop in real time. When they’re about to move on to the next piece we can provide a double check and say, ‘Hey, we think you missed step 8.’ That can save a huge amount of pain. It might be as simple as plugging in a cable, but catching it there is huge — if it’s after the vehicle has been assembled, you’d have to tear it down again.”
This kind of body tracking exists in various forms and for various reasons; Veo Robotics, for instance, uses depth sensors to track an operator and robot’s exact positions to dynamically prevent collisions.
But the challenge at the industrial scale is less “how do we track a person’s movements in the first place” than “how can we easily deploy and apply the results of tracking a person’s movements.” After all, it does no good if the system takes a month to install and days to reprogram. So Invisible AI focused on simplicity of installation and administration, with no code needed and entirely edge-based computer vision.
“The goal was to make it as easy to deploy as possible. You buy a camera from us, with compute and everything built in. You install it in your facility, you show it a few examples of the assembly process, then you annotate them. And that’s less complicated than it sounds,” Danziger explained. “Within something like an hour they can be up and running.”
Once the camera and machine learning system is set up, it’s really not such a difficult problem for it to be working on. Tracking human movements is a fairly straightforward task for a smart camera these days, and comparing those movements to an example set is comparatively easy, as well. There’s no “creativity” involved, like trying to guess what a person is doing or match it to some huge library of gestures, as you might find in an AI dedicated to captioning video or interpreting sign language (both still very much works in progress elsewhere in the research community).
As for privacy and the possibility of being unnerved by being on camera constantly, that’s something that has to be addressed by the companies using this technology. There’s a distinct possibility for good, but also for evil, like pretty much any new tech.
One of Invisible’s early partners is Toyota, which has been both an early adopter and skeptic when it comes to AI and automation. Their philosophy, one that has been arrived at after some experimentation, is one of empowering expert workers. A tool like this is an opportunity to provide systematic improvement that’s based on what those workers already do.
It’s easy to imagine a version of this system where, like in Amazon’s warehouses, workers are pushed to meet nearly inhuman quotas through ruthless optimization. But Danziger said that a more likely outcome, based on anecdotes from companies he’s worked with already, is more about sourcing improvements from the workers themselves.
Having built a product day in and day out year after year, these are employees with deep and highly specific knowledge on how to do it right, and that knowledge can be difficult to pass on formally. “Hold the piece like this when you bolt it or your elbow will get in the way” is easy to say in training but not so easy to make standard practice. Invisible AI’s posture and position detection could help with that.
“We see less of a focus on cycle time for an individual, and more like, streamlining steps, avoiding repetitive stress, etc.,” Danziger said.
Importantly, this kind of capability can be offered with a code-free, compact device that requires no connection except to an intranet of some kind to send its results to. There’s no need to stream the video to the cloud for analysis; footage and metadata are both kept totally on-premise if desired.
Like any compelling new tech, the possibilities for abuse are there, but they are not — unlike an endeavor like Clearview AI — built for abuse.
“It’s a fine line. It definitely reflects the companies it’s deployed in,” Danziger said. “The companies we interact with really value their employees and want them to be as respected and engaged in the process as possible. This helps them with that.”
The $3.6 million seed round was led by 8VC, with participating investors including iRobot Corporation, K9 Ventures, Sierra Ventures and Slow Ventures.
Advance government procurements of motor vehicles will total 33,500 units in May – July, Russian Minister of Industry and Trade Denis Manturov said on Sunday in an interview with the Rossiya-24 TV Channel.
“We provisionally worked out this program related to the automotive industry support in the government. This pertains to other government procurements also. About 33,500 automobiles of various authorities, organizations, including state-owned companies and companies with the state participation, should be provided in total over May, June and July,” Manturov said, TASS reports.
Government authorities will start procurements of automobiles scheduled to 2021 and 2022 as early as in the second quarter of 2020, as reported earlier. “This will actually provide the automotive industry to pass through this challenging period, specifically May, June, and July,” the minister added.
Manturov noted that Russian automakers will be 100% loaded with public transport production in 2020.
“I can confidently state that the utilization will be 100% in 2020, particularly for the public transport, for buses of different classes,” the Minister said. “This is a helping hand and concurrently the support for the automotive industry and the upgrade of the public transport for regions,” Manturov pointed out.
Automobile producers will also deal with manufacturing of ambulance cars and reanimobiles, the Minister said. “Speaking about reanimobiles, there will be about 1,400 units. We already have experience of implementing such tasks in prior years. These are centralized supplies,” Manturov said.
Late last year and early this year, research was coming out about the supply chain market, and it all looked relatively straightforward. For instance, last summer, Grand View Research predicted that one slice of the global supply-chain market, supply-chain analytics, would exceed $9.8 billion by 2025, growing at a CAGR (compound annual growth rate) of 16.4% from 2019 to 2025. The top factor thought to restrict growth during this timeframe was concerns over data security. And while, at the time, this analysis was solid, it didn’t—and, in fact, couldn’t—take into account what was coming just around the bend.
In the U.S., and worldwide, a curveball in the form of COVID-19 is now complicating the supply-chain picture. In some cases, it’s shedding light on how disjointed supply chains really are. In other cases, it’s throwing into sharp relief how critical flexibility can be within the supply chain. What lessons will this pandemic force the supply chain industry to learn, and will technology play more, less, or roughly the same role in future supply chains as it did pre-COVID-19? What will it take to jumpstart supply chains post-COVID-19? These are some of the questions the industry will be asking for years to come as part of the extensive ripple effects the 2020 coronavirus pandemic will cause in the next decade and, possibly, beyond.
A supply chain is a system of organizations, people, activities, information, and resources involved in supplying a product or service to a consumer. In industries like food and food service, the supply chain includes players as varied as the farmer who’s growing or producing the food products themselves to the transportation company that’s bringing things like milk and eggs to grocery stores and restaurants that ultimately cater to end users: consumers. Several trends during the past several years have been shifting how supply-chains operate, including an overall diversification of consumer preferences, consumer demand for traceability (especially in food and food-service supply chains), consumer and regulatory demand for sustainability in the production and transportation of all kinds of goods, and the use of technology to manage supply chain operations.
Since COVID-19 began to hit the U.S. hard in March, some supply chains were immediately and directly affected by the illness as workers became sick and were quarantined. Many more were affected as supply and demand began to shift in topsy-turvy ways as the economy underwent a major and swift transition. Restaurants and retail stores shut down, and consumers flooded grocery stores for essential items like canned goods and paper products. As businesses were forced to lay off workers, consumers closed their pocket books to entire categories of products and services.
The apocalyptic sense felt upon entering grocery stores with empty shelves left many wondering what was going on in the supply chain. While on the surface, it looked like food shortages, the problem really was and is with the supply chain. On one hand, demand from restaurants has plummeted, and on the other, consumers stockpiling food to avoid coming back to the store as often as usual (or, in worse-case scenarios, hoarding food), are throwing historic data and trends’ value out the window. And yet, technology will be key to both managing this rough spot and jumpstarting supply chains once economies begin to go back to “normal”—whatever the new normal will be.
Supply-chain players prepared to offer transparency, communication, and flexibility are best positioned to limit the disruption to operations during times like the current COVID-19 outbreak. Lessons learned during these hard times will hopefully encourage more supply chains to adopt practices and technologies that will make their link in the chain more resilient next time around.
Want to tweet about this article? Use hashtags #IoT #sustainability #AI #5G #cloud #edge #digitaltransformation #machinelearning #supplychain #foodservice #analytics #COVID19 #coronavirus #retail
While some U.S. investors might have taken comfort from China’s rebound, we still find ourselves in the early innings of this period of uncertainty.
Some epidemiologists have estimated that COVID-19 cases will peak in April, but PitchBook reports that dealmaking was down -26% in March, compared to February’s weekly average. The decline is likely to continue in coming weeks — many of the deals that closed last month were initiated before the pandemic, and there is a lag between when deals are made and when they are announced.
However, there’s still hope. A recent report concluded that because valuations are lower and there’s less competition for deals, “the best-performing vintages tend to be those that invest at the nadir of a downturn and into the early stage of recovery.” There are countless examples from the 2008 recession, including many highly valued VC-backed businesses such as WhatsApp, Venmo, Groupon, Uber, Slack and Square. Other early-stage VCs seem to have arrived at a similar conclusion.
Also, early-stage investing seems more resilient. During the last recession, angel and seed activity increased 34% as interest in the stage boomed during a period of prolonged growth.
Furthermore, there is still capital to be deployed in categories that interested investors before the pandemic, which may set the new order in a post-COVID-19 world. According to data provider Preqin Ltd., VC dry powder rose for a seventh consecutive year to roughly $276 billion in 2019, and another $21 billion were raised last quarter. And looking at the deals on the early-stage side that were made year to date, especially in March, the vertical categories that garnered the most funding were enterprise SaaS, fintech, life sciences, healthcare IT, edtech and cybersecurity.
Image Credits: PitchBook
That said, if VCs have the capital to deploy and are able to overcome the obstacle of “having never met in person,” here are six investment trends that could emerge when the pandemic is over.
The Ministry of Industry and Trade of the Russian Federation plans to enter the weekly production of about 300 mechanical ventilation devices (IVL) from next week, TASS reported citing Minister of Industry and Trade Denis Manturov.
“From 4 to 5 mechanical ventilation was performed per week. Today, this week, we will reach 200 units, and next week we expect to reach 300, or maybe a little more,” he said.
“And in total, the order that we have from the healthcare system is there, in all regions, more than 5 thousand units – we must fulfill this volume in three months,” the head of the ministry added.
In 2020, the Government of the Russian Federation will allocate 7.5 billion rubles to the Ministry of Industry and Trade from the reserve fund for the purchase of ventilators and extracorporeal membrane oxygenation. It is planned to purchase at least 5.7 thousand devices, the sole supplier is the JSC “Radioelectronic Technology Concern” (KRET) of the state corporation Rostec.
Russian enterprises are ahead of the plan of the Ministry of Industry and Trade and are already producing over 8 million masks per day, said the head of the department Denis Manturov, as reported by RIA Novosti.
“Masks alone now produce more than 8 million per day. This is already more than what we promised to release by the end of the month,” said Manturov, whose words are quoted in the press release of the ministry.
The demand for medical masks has increased in Russia due to the pandemic of the coronavirus COVID-19. Light industry enterprises and even correctional institutions joined the production of masks in Russia.
