Arm today announced plans to spinoff its two IoT business, a move that would effectively transfer the divisions under the broader umbrella of Softbank Group core, which purchased the chip designer back in 2016. The move comes as Arm seeks to focus its efforts exclusively on the semiconductor IP business that has made the company a ubiquitous presence in the mobile world.
The transfer is pending addition review from the company’s board, along with standard regulatory reviews — though Arm says it expects the move to be completed before the end of September of this year. While it would effectively remove the IoT Platform and Treasure Data businesses from its brand, the company says it plans to continue to collaborate with the ISG (IoT Services Group) businesses.
“Arm believes there are great opportunities in the symbiotic growth of data and compute,” ARM CEO Simon Segars said in a release tied to the news. “SoftBank’s experience in managing fast-growing, early-stage businesses would enable ISG to maximize its value in capturing the data opportunity. Arm would be in a stronger position to innovate in our core IP roadmap and provide our partners with greater support to capture the expanding opportunities for compute solutions across a range of markets.”
Arm’s IoT business has seen quite a bit of success, with its technologies shipping on billions of devices and the planned goal of one trillion expected next decade. These days, however, it seems content to focus on servers, desktops and edge computing, in addition to mobile products.
Tyler Cracraft is an electronic engineer turned solution architect at Advantech who has more than a decade of experience working in the electronics technology industry.
If you’re a business owner or investor and are wondering about the long-term impacts of the COVID-19 pandemic on the business world, you’re not alone.
Today’s business leaders have been plunged into the deep end of telecommuting with little notice, and the way we do business has been impacted at almost every level. Travel is restricted, meetings are virtual and delivery of goods and even raw materials is being delayed. While some industries that depend on large gatherings are seeing extremely difficult challenges due to the pandemic, others such as the tech industry, see the opportunity and responsibility for innovation and growth.
As many states begin phased reopening, companies are trying to determine what the workplace and business environment will look like in a post-quarantine world. The first obvious step is the integration of personal protective equipment (PPE). Sanitization and face masks will become required and nonessential face-to-face meetings will be a thing of the past, along with shaking hands.
Additionally, relationship-driven careers such as sales and recruiting will have to find new ways to connect to be successful. Social distancing rules will have to be established, which may include employees coming in alternate days while telecommuting the other days of the week to keep offices at reduced capacity. Large offices of 10 or more may implement thermographic camera technology for fever screening or other real-time technology-based health screenings.
One thing is for sure: IoT devices that enable social distancing will become an integral part of reopening businesses, facilitating sales connections and embracing a different way of living.
Solutions for social distancing
There are a variety of IoT devices available that can help business leaders successfully implement social distancing in their offices. Thermographic camera technology coupled with facial recognition can create a baseline for each employee and then assist in determining if an employee has a temperature outside of their norm. Other remote health monitoring may also take place with healthcare providers, helping employees determine on a daily basis if they are well enough to go into work.
Cybersecurity is by far the most important area in any industry. Without it, we would be in hacker open season.
But cybersecurity is difficult to get right. One wrong move and you can leave the door open for data breaches, ransomware, and nation state-backed espionage. That’s why there’s such an intense focus on cybersecurity from an investor’s point of view. How does an investor know what’s a worthwhile security solution and not snake oil? And in an already saturated security startup space, who can you trust to keep your company’s data safe?
These are just some of the questions we want answers to.
Every few months we check in with some of the leading investors in cybersecurity to gauge the heat (or chill) of the market, see what trends are making waves, and understand some of the challenges in a busy startup world.
This time around, we spoke to a dozen cybersecurity VCs to hear their thoughts on what they’re most excited about, cybersecurity valuations (in the age of pandemic, no less), which companies are sparking investors’ interests, and the kinds of startups that aren’t.
Per info Guan provided, Rokid’s T1 thermal glasses use an infrared sensor to detect the temperatures of up to 200 people within two minutes from up to three meters away. The devices carry a Qualcomm CPU, 12 megapixel camera and offer augmented reality features — for hands free voice controls — to record live photos and videos.
The Chinese startup (with a San Francisco office) plans B2B sales of its wearable devices in the U.S. to assist businesses, hospitals and law enforcement with COVID-19 detection, according to Guan.
Rokid is also offering IoT and software solutions for facial recognition and data management, as part of its T1 packages.
Image Credits: Rokid
The company is working on deals with U.S. hospitals and local municipalities to deliver shipments of the smart glasses, but could not disclose names due to confidentiality agreements.
One commercial venture that could use the thermal imaging wearables is California based e-commerce company Weee!.
The online grocer is evaluating Rokid’s T1 glasses to monitor temperatures of its warehouse employees throughout the day, Weee! founder Larry Liu confirmed to TechCrunch via email.
On procedures to manage those who exhibit COVID-19 related symptoms, that’s something for end-users to determine, according to Rokid. “The clients can do the follow-up action, such as giving them a mask or asking to work from home,” Guan said.
The T1 glasses connect via USB and can be set up for IoT capabilities for commercial clients to sync to their own platforms. The product could capture the attention of U.S. regulators, which have become increasingly wary of Chinese tech firms’ handling of American citizen data. Rokid says it doesn’t collect info from the T1 glasses directly.
“Regarding this module…we do not take any data to the cloud. For customers, privacy is very important to them. The data measurement is stored locally,” according to Guan.
Image Credits: Rokid
Founded in 2014 by Eric Wong and Mingming Zhu, Rokid raised $100 million at the Series B level in 2018. The business focuses primarily on developing AI and AR tech for applications from manufacturing to gaming, but developed the T1 glasses in response to China’s COVID-19 outbreak.
The goal was to provide businesses and authorities a thermal imaging detection tool that is wearable, compact, mobile and more effective than the common options.
Large scanning stations, such as those used in some airports, have drawbacks in not being easily portable and handheld devices — with infrared thermometers — pose risks.
“You have to point them to people’s foreheads…you need to be really close, it’s not wearable and you’re not practicing social distancing to use those,” Guang said.
Rokid pivoted to create the T1 glasses shortly after COVID-19 broke out in China in late 2019. Other Chinese tech startups that have joined the virus-fighting mission include face recognition giant SenseTime — which has installed thermal imaging systems at railway stations across China — and its close rival Megvii, which has set up similar thermal solutions in supermarkets.
On Rokid’s motivations, “At the time we thought something like this can really help the frontline people still working,” Guang said.
The startup’s engineering team developed the T1 product in just under two months. In China, Rokid’s smart glasses have been used by national parks staff, in schools and by national authorities to screen for COVID-19 symptoms.
Source: Johns Hopkins University of Medicine Coronavirus Research Center
The growth rate of China’s coronavirus cases — which peaked to 83,306 and led to 3,345 deaths — has declined and parts of the country have begun to reopen from lockdown. There is still debate, however, about the veracity of data coming out of China on COVID-19. That led to a row between the White House and World Health Organization, which ultimately saw President Trump halt U.S. contributions to the global body this week.
As COVID-19 cases and related deaths continue to rise in the U.S., technological innovation will become central to the health response and finding some new normal for personal mobility and economic activity. That will certainly bring fresh facets to the common tech conundrums — namely measuring efficacy and balancing benefits with personal privacy.
For its part, Rokid already has new features for its T1 thermal smart glasses in the works. The Chinese startup plans to upgrade the device to take multiple temperature readings simultaneously for up to four people at a time.
“That’s not on the market yet, but we will release this very soon as an update,” said Rokid’s U.S. Director Liang Guan .
Machine learning (ML) based products have particular characteristics and challenges, from data quality to counterfactual problems and explainability. What then are the implications of ML products for team structure, focus, and hiring?
Data science jobs are increasing at around 30% year on year, and if you don’t already have a data scientist in your ranks there’s a good chance you will soon. Perhaps you already have a number of data products you use to segment customers, predict prices or improve your product in other ways. Or maybe your core product is the machine learning, making recommendations and predictions for healthcare, security, ad tech or other applications.
ML Challenges
ML lives and dies by the data it relies on; garbage in, garbage out. The wrong decision can be made if data is missing or biased, if there are collisions, or if data is received at the wrong time or in the wrong order. Furthermore, many ML models’ predictions will be used to determine a certain path in real time or near-real time. So once the data has been fed in and a decision has been made, it’s too late for a fix. The user has already been ushered down the wrong path.
Going all out for accuracy without considering how your users will digest the output can undermine trust in your product. How can you trust this thing if you can’t understand it?
Whether the data is good or bad, once interventions are made a counterfactual problem arises. The path having been taken, you cannot prove what would have happened otherwise – and by extension whether your product was right.
A/B testing can address this, in principle: let a small number of users through without intervention, and see if your predictions were correct. But in practice this can be a tough sell in cases where there is a very measurable cost – financial or otherwise – of not intervening. And even if agreed, there may be a temptation to constrain the test artificially and thereby undermine its premise.
Then there’s the well-publicised black box challenge of ML products. We may know the input and the output, but not what the algorithm did in between. This is the question of “explainability”: the extent to which a human can analyse and explain the reasoning behind a prediction made by ML. In the UK, the Information Commissioner’s Office and The Alan Turing Institute are developing practical guidance and checklists to help with this challenge.
Different ML techniques have different levels of explainability. For example, decision trees are more explainable than neural networks, which are more sophisticated. There’s often a tradeoff between accuracy and explainability. Going all out for accuracy without considering how your users will digest the output can undermine trust in your product. How can you trust this thing if you can’t understand it?
If you’re offering a B2B service, this can affect both the buyer of your product and the end users. The end users may perceive machine learning as a threat, rightly or wrongly. Even products which set out to augment existing teams will change how they work, and change is hard. For example, an insurance analyst may shift from personally reviewing applications to directing ML-driven review tools and investigating macro level trends.The perceived impact needs to be thought through and mitigated.
Decision trees are more explainable than neural networks (Image: Shutterstock)
Team Structure
Given these particular challenges, how should the ML product be managed?
Data Quality
To address the critical role of data quality, you need to focus on how data is received and stored, and who is responsible. Data engineering is commonly thought of as the plumbing, but I don’t think that really does it justice; it’s also the architectural plans and foundations of the house. Without good data engineering the edifice will fall apart. So it’s important to consider who will work on it, their level of experience, and how much time should be dedicated to it. Rather than distribute the responsibility, it may be beneficial to have a centre of excellence in the interests of consistency, efficiency and ensuring the data gets the attention it deserves.
At Ravelin, for example, one of our teams is explicitly focused on data engineering. The team supports the needs of multiple other teams such as data scientists, analysts, and integration engineers. This runs the gamut of raw data being received to insights being extracted. In between, data may need to be normalised to ensure consistency and aid comparison; it may need to be enriched with complementary data sources; numerical values may need to be fed into calculations to populate a different data field; the list of actions goes on. And it’s not just the data this team grapples with, it’s also the surrounding infrastructure and pipeline for training and deploying models.
Data Science and Analysis
When it comes to data science, you could choose to create a centralised shared service team, or else distribute data scientists in multidisciplinary teams. Each approach has trade-offs.
Ensure close collaboration between data science, product and engineering, regardless of structure (Image: Shutterstock)
With a centralised team, especially one where the data scientists are largely working on the same problem area, the central model makes it easier for lessons to be shared daily and applied rapidly. It creates a shared purpose and a focal point of responsibility for the accuracy of the ML: the buck stops here. It may also be the only option, depending on the size of the company or number of data scientists. The risk is the team becomes a functional silo, not working closely enough with product-engineering teams, with goals and priorities which are not in sync.
Conversely, embedding the data scientists in cross-functional teams can help with alignment and the early stages of new product development. That team will have reduced dependencies and increased autonomy. But this may not fit so well with the skillsets, areas of interest and future development of the data scientists. It may disrupt the balance of operational data science work, such as constantly training new models, and new product development work. Asking a data scientist to focus only on one area might be equivalent to asking a product manager to only look at the UX, or only the tech, or only the business.
The most important thing is close collaboration between data science, product and engineering, regardless of structure. Make sure you don’t miss out on the considerable brains trust of the data science team, including in less obviously “data sciencey” areas. On the one hand, you can add further diversity of thought to your decision-making. And on the other, you should ensure you aren’t making product changes with unforeseen impact on the quality of your ML. For example, you could unwittingly stop models from learning by encouraging user behaviour which cuts out particular data signals.
Another consideration is who is responsible for data analysis. Just the data science team? Or are there other people, teams or roles which could augment the machines with the benefit of real-world context, intuition and interpretation? You might look at existing analyst or customer care teams. At Ravelin we created a specific investigations team for this purpose, a blend of client support, fraud investigation and data science.
Product Management
I don’t think a particular profile of product manager is needed for ML products, certainly not across the board. We’ve recruited a mix of technically oriented product managers and generalist product managers.
Good analytical skills and attention to detail go a long way (Image: Shutterstock)
With ML products there is more emphasis on the data and APIs, and good analytical skills and attention to detail go a long way. Likewise proficiency with Chrome Developer Tools and SQL. You’ll regularly need to tell the difference between a bug and an issue with the underlying data. Then think of better ways to handle that data.
But the product management fundamentals for ML products remain the same. Good teamwork and communication, a large dose of curiosity, and an eagerness to learn are more than enough for someone to be successful working on ML products.
In the first month you’ll have to get your head round many new concepts. The existence of something called a confusion matrix will never seem more apt. But soon things start falling into place. You’ll figure out what you need to know, and what you don’t need to know. And it is of course a great place to be as a product manager: in an increasingly important domain with interesting challenges and many opportunities to learn and grow.
The internet of things (IoT) market is expanding at a rate where distinguishing it as a separate category is beginning to seem a bit absurd. Increasingly, new products — and updates of existing ones — are smart and/or connected. One company is changing the fundamental calculus behind this shift by lowering the barrier considerably when it comes to what it costs to make something ‘smart,’ both in terms of the upfront bill of materials, along with subsequent support and development costs.
MicroEJ CEO Fred Rivard took me through his company’s history from its founding in 2004 until now. Much of those earlier years were spent in development, but since around 2012 or so, the French company has been deploying for IoT devices what Android is to smartphones — a flexible, extensible platform that can operate on a wide range of hardware profiles while being relatively easy to target for application and feature developers. MicroEJ takes the ‘code once, deploy anywhere’ maxim to the extreme, since its platform is designed from the ground up to be incredibly conservative when it comes to resource consumption, meaning it can run on hardware with as little as one-tenth or more the bill of materials cost of running more complex operating platforms — like Android Things, for instance.
“We take category of device where currently, Android is too big,” Rivard said. “So it doesn’t fit, even though you would like to have the capability to add software easily devices, but you can’t because Android is too big. The cost of entry is roughly $10 to $15 per unit in hardware and bill of material — that’s the cost of Android […] So it would be great to be able to run an Android layer, but you can’t just because of the cost. So we managed to reduce that cost, and to basically design a very small layer that’s1000 times smarter than Android.”
Every year, Consumer Electronics Show attendees receive a branded backpack, but this year’s edition was special; made out of transparent plastic, the bag’s contents were visible without the wearer needing to unzip. It isn’t just a fashion decision. Over the years, security has become more intense and cumbersome, but attendees with transparent backpacks didn’t have to open their bags when entering.
That cheap backpack is a metaphor for an ongoing debate — how many of us are willing to exchange privacy for convenience?
Privacy was on everyone’s mind at this year’s CES in Las Vegas, from CEOs to policymakers, PR agencies and people in charge of programming the panels. For the first time in decades, Apple had a formal presence at the event; Senior Director of Global Privacy Jane Horvath spoke on a panel focused on privacy with other privacy leaders.
