Arm brings AI and machine learning to IoT and the edge – Blog – 10 minute

Why it matters: For a company that doesn’t manufacture anything, Arm has a surprisingly large and broad impact, not only on the chip industry, but the overall tech industry and, increasingly, many other vertical industries as well. The company—which creates semiconductor chip designs that it licenses as intellectual property and then Arm’s customers use the designs to build chips—is the brains behind virtually every smartphone ever made.
In addition, it has a small but growing market in data center and other network infrastructure equipment and is the long-time leader in intelligent devices of various types—from toys to cars and nearly everything in between; essentially the “things” part of IoT (Internet of Things).
As a result, it’s not terribly surprising to see the company pushing ahead on new innovations to power more devices. What is unexpected about Arm’s latest announcements, however, is the degree of performance that it’s enabling in microcontrollers—tiny chips that power billions of devices. Specifically, with the launch of its new Cortex-M55 Processor, companion Ethos-U55 microNPU (Neural Processing Unit) accelerator, and new machine learning (ML) software, Arm is promising a staggering 480x improvement in ML performance for a huge range of low-power applications.

That’s not the kind of performance numbers you typically hear about in the semiconductor industry these days. Practically speaking, it turns something that was nearly impossible into something very doable.
More importantly, because microcontrollers are the tiny, unsung heroes powering everything from connected toothbrushes to industrial equipment, the potential long-term impact could be huge. Most notably, the addition of AI intelligence to all these types of “things” offers the promise of finally getting the kind of smart devices that many hoped for in areas from smart home to factory automation and beyond. Imagine adding voice control to the smallest of devices or being able to get advanced warnings about potential part failures in slightly larger edge computing equipment through onboard predictive maintenance algorithms. The possibilities really are quite limitless.
Practically speaking, it turns something that was nearly impossible into something very doable.
The announcements are part of an overall strategy at Arm to bring AI capabilities to its full range of IP designs. Key to that is work the company is doing on software and development tools. Because Cortex-M series designs have been around for a long time, there’s a large base of applications that device designers can use to program them. However, because a great deal of ML and AI-based algorithm work is being created in frameworks, such as TensorFlow, the company is also bringing support for its new IP designs into TensorFlow Lite Micro, which is optimized for the types of smaller devices for which these new chips are intended.

In addition to software, there are several different hardware-centric capabilities that are worth calling out. First, the Cortex-M55 is the first microcontroller design to incorporate support for the company’s Helium vector process technology, previously only found on larger Arm CPU cores. The M55 also includes support for Arm Custom Instructions, an important new capability that lets chips designers create custom functions that can be optimized for specific workloads.
The new Ethos-U55 is a first of its kind dedicated AI accelerator architecture that was designed to pair with the M55 for devices in which the 15x improvement in ML performance that the M55’s new design offers is not enough. In addition, the combination of the M55 and the U55 was specifically intended to offer a balance of scalar, vector, and matrix processing, which is essential to efficiently running a wide range of machine-learning-based workloads.
Of course, there are quite a few steps between releasing new chip IP designs and seeing products that leverage these capabilities. Unfortunately, that means it will likely be sometime near the end of 2021 and into 2022 before we can really see the promised benefits of a nearly 500x improvement in machine learning performance. Plus, it remains to be seen how challenging it will be for low-power device designers to create the kinds of ML algorithms they’ll need to make their devices truly smart. Hopefully, we’ll see a large library of algorithms developed so that device designers with little to no previous AI programming experience can leverage them.
Ultimately, the promise of bringing machine learning to small, battery-powered devices is an intriguing one that opens up some very interesting possibilities for the future. It will be interesting to see how the “things” develop.
Bob O’Donnell is the founder and chief analyst of TECHnalysis Research, LLC a technology consulting and market research firm. You can follow him on Twitter @bobodtech. This article was originally published on Tech.pinions.

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First drug developed using machine learning enters clinical trials – Blog – 10 minute

What just happened? Of all the domains where machine learning is expected to be revolutionary, medicine is perhaps the most universal. In a major new milestone, a drug developed using machine learning is about to enter human trials.
Before a new medicine enters human trials, there is typically three to five years of work behind the scenes, researching causes for diseases and compounds that may help treat them. But working with British AI startup Exscientia, a Japanese drug development company called Sumitomo Dainippon Pharma Co. is about to start phase 1 clinical trials after only 12 months.
The drug in question is DSP-1181, a prospective treatment for obsessive-compulsive disorder (OCD). OCD affects millions of people worldwide, to varying degrees, and can be debilitating in its psychological effects.
Exscientia, based in Oxford, UK, operates an exciting machine learning platform called Centaur Chemist. The platform allegedly takes years off the time required to research new compounds, by combining A.I. techniques with existing knowledge of how medicines interact with the human body.
The benefit of machine learning is that it can happen virtually, and far quicker than scientists are able to work in the real world. The platform can analyze millions of molecular combinations and attempt to identify which may be the safest and most effective in treating a given disease.

Perhaps even more important is the potential savings associated with using machine learning to develop new medicines. Typically, it costs over $1 billion to bring a new drug through from conception to market, with a lot of those costs borne out during the research phases. But taking out years of painstaking research will save both time and money, speeding up development and freeing up resources to develop yet more medicines.
There’s a lot riding on Exscientia and Sumitomo Dainippon’s trial. The first phase is to check how the drug affects the body, and how the body metabolises the drug. So this will not prove the medication’s efficacy.
But if DSP-1181 is shown to be safe, phases two and three can proceed, to see whether the drug can help OCD patients in the real world. And if it does, we’ll witness the dawn of machine learning in medicine.

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Become a data analytics expert by learning Tableau, Python, Excel, and MongoDB – Blog – 10 minute

Data analytics will help enhance your problem solving and data interpretation skills to drive better business decisions in your organization.
The Data Analytics Expert Certification Bundle combines 5 courses to get you up to speed on the best tools of the trade including Tableau, Excel, Python, and MongoDB. This bundle normally retails for over $2,500, but it’s currently on sale for just $49.
Knowledge is a powerful currency, so why not make a career out of gaining knowledge and understanding how to use it. Understand the complete data analytics lifecycle, how data visualization and data science methodologies can be used to drive better business decisions.
This 5-part bundle includes:

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For a limited time, this 5-part package will only cost you $49. That’s 98% off the original price.
Note: TechSpot may receive a commission for sales from links on this post through affiliate programs.

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How NAB uses machine learning to find faked loan docs – Finance – Cloud – Security – Software- Tempemail – Blog – 10 minute

NAB has finally revealed the nature of a fraud-related machine learning use case, with the algorithm being used to detect people that file fake documentation in support of loan applications.
Chief data officer Glenda Crisp first disclosed the fraud-related use case at AWS Summit in Sydney back in May 2019, but largely declined to discuss it.
“We’ve been able to do some interesting things around fraud and I don’t like to talk about this too much,” she said at the time.
Speaking at AWS re:Invent 2019 in Las Vegas last month, Crisp provided the first indication of where the bank is targeting instances of fraud with its machine learning efforts.
“In terms of machine learning models, we’ve been working on building this capability for a little while now,” she said.
“We’re starting in the areas you would expect a bank to start in: anti-money laundering, cyber and fraud.
“Let me just talk about fraud quickly. This may be a shock to some of you in the room, but there are people who actually lie on their loan applications. And not only do they lie, they send us fake documentation like fake payslips.
“So we are building a machine learning model – it’s actually fairly advanced through its training right now – to try to identify fake and falsified documentation, and we’re seeing some really good improvements over the traditional models that we’ve used.”
Crisp also discussed the existence of a second machine learning model that is performing “topic and theme analytics around customer complaints.”
“We get customer complaints, and we actually would like to know what’s driving those complaints,” Crisp said.
“So we have a machine learning model that’s been running for a few months now on those [complaints].
“What I, as a CDO, am most interested in and I track most closely is the percentage of customer complaints related to data quality and so that’s a number that I want to drive down because I don’t think that should happen. 
“I think our customers should be able to trust us for good quality data.”
Experimental ‘labs’
NAB is using AWS SageMaker to allow around 400 data scientists and analytics team members to build, train and deploy machine learning models.
This forms part of a toolkit that NAB calls its Discovery Cloud or NDC.
“This is where we are building machine learning models and predictive analytics,” Crisp said.
Crisp said that data scientists were able to spin up temporary “data labs” to experiment building models for up to 90 days – sometimes longer if required.
She said there were currently 55 data labs running within NAB, “each of them with usually one, maybe two use cases going at any point in time.”
The creation and destruction of data labs happened automatically using AWS services, and they contained appropriate guardrails to ensure safe experimentation. 
The automatic time limitation was intended to ensure that models were not run permanently in the labs, but were instead migrated into regular production environments if they were deemed ready.
“[Labs are] not meant to be running models all the time,” Crisp said.
“This is meant as a development or an experimentation area, so if you do build a model, and you do want it to go to production, we have a path to production and we have a production-run environment that it’ll then live in, and we have all the right operational controls around that.”
Crisp noted that NDC is the portion of NAB’s data environment that is most likely to change rapidly.
“This is probably the part of the architecture that shifts and is trying out new things the most,” she said.
“We’re constantly looking at new ways to do things. 
“We’ve got a PoC going with H2O.ai, and Databricks is something else that we’re exploring as well. So you’ll see us trying things in here. 
“This part of the architecture especially will change pretty rapidly over the next six to 12 months.”

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New machine learning algorithms to spot online trolls: Study- Tempemail – Blog – 10 minute

Researchers, including two of Indian-origin, have demonstrated that machine-learning algorithms can monitor online social media conversations as they evolve, which could one day lead to an effective and automated way to spot online trolling. Prevention of online harassment requires rapid detection of offensive, harassing, and negative social media posts, which in turn requires monitoring online interactions.
Current methods to obtain such social media data are either fully automated and not interpretable or rely on a static set of keywords, which can quickly become outdated. Neither method is very effective, according to Indian-origin researcher Maya Srikanth, from California Institute of Technology (Caltech) in the US.
“It isn’t scalable to have humans try to do this work by hand, and those humans are potentially biased,” Srikanth said.
“On the other hand, keyword searching suffers from the speed at which online conversations evolve. New terms crop up and old terms change meaning, so a keyword that was used sincerely one day might be meant sarcastically the next,” she added.
According to the study, the research team used a GloVe (Global Vectors for Word Representation) model to discover new and relevant keywords.
GloVe is a word-embedding model, meaning that it represents words in a vector space, where the “distance” between two words is a measure of their linguistic or semantic similarity.
Starting with one keyword, this model can be used to find others that are closely related to that word to reveal clusters of relevant terms that are actually in use. For example, searching Twitter for uses of “MeToo” in conversations yielded clusters of related hashtags like “SupportSurvivors,” “ImWithHer,” and “NotSilent.”
This approach gives researchers a dynamic and ever-evolving keyword set to search. The project was a proof-of-concept aimed at one day giving social media platforms a more powerful tool to spot online harassment.
“The field of AI research is becoming more inclusive, but there are always people who resist change,” said researcher Anima Anandkumar, who in 2018 found herself the target of harassment and threats online because of her successful effort to switch to an acronym without potentially offensive connotations.
“It was an eye-opening experience about just how ugly trolling can get. Hopefully, the tools we’re developing now will help fight all kinds of harassment in the future,” she added.
The study was presented on December 14 last year at the AI for Social Good workshop at the 2019 Conference on Neural Information Processing Systems in Vancouver, Canada.

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SUSE and Karunya Institute of Technology and Sciences collaborate to enhance cloud and open source learning- Tempemail – Blog – 10 minute

The SUSE Academic Program, the education arm of SUSE, and Karunya Institute of Technology and Sciences (KITS), have signed an MoU to collaborate in providing Linux and open source learning and skills to students. The program aims to provide aspiring professionals with essential technical expertise and help them leverage opportunities in the cloud job market via “SUSE Certified Administrator (SCA) in Enterprise Linux” certification.
SUSE will support the program with all course materials for cloud-related technologies such as DevOps, cloud application development, cloud administration, and enterprise Linux.
Marco Kraak, Vice President of Channel, SUSE EMEA and APJ, said, “As a leader in open source, SUSE understands the changing dynamics of the IT industry. Through the SUSE Academic Program, we have been supporting academia to meet the changing demands of the digital economy by providing open source knowledge, training materials and an affordable education opportunity that benefit students as they explore job possibilities in the fast-growing technology space.”
Rajarshi Bhattacharyya, Country Manager, SUSE India, added, “At SUSE, we believe that by educating and preparing the next generation of professionals, we are ensuring the future growth and adoption of open source. Our collaboration with KITS will pave the way for learning and skill development in this area.”
As part of the program, SUSE will provide all staff and students with full access to an on-demand technical training course library, curriculum, virtual labs and educational resources. SUSE will also endorse and support B.Tech – Computer Science and Engineering and M.Tech – Computer Science and Engineering courses offered by KITS.
Dr. P Mannar Jawahar, Vice-Chancellor, KITS, said, “The education system in India needs enormous transformation to be on par with the current trends in cloud-related technologies. The demand for such professionals is outstripping supply and we are delighted to partner with SUSE on a mission to create experts in these technologies. The availability of job-ready graduates who are hands on with technology will help to fill the skill gap in the IT industry. We look forward to a fruitful and continuous relationship with SUSE.”
As part of the collaboration, the faculties will also be offered technical experience and practice through a “Train the Trainer” approach. SUSE has already been providing trainer programs exclusively on KITS premises.
Founded in 2017, the SUSE Academic Program includes over 800 universities, schools, libraries and other academic institutions across the globe. It aims to double the number of participants in the next six months.

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Deep Learning: What is it and why is it in demand?- Tempemail – Blog – 10 minute

The human brain is complicated and for all the right reasons. A pathway of neurons, millions of cells, creating a response to stimuli and observation, all of this working simultaneously to help our brain give an appropriate response. Furthering its greatness, the brain is constantly observing and learning. 
A well-known learning method- ‘learning by example’ helps us develop an attitude or coping mechanism towards something we haven’t fully experienced but have a lot of information about. When we use the same logic for machines, deep learning comes closest to this. 
What is deep learning and how does it work?
Stemming out of the machine learning algorithm category, deep learning is a computer or a machine’s ability to learn things by example or data. It eliminates the need for manual feature extraction as it learns features directly from the data. 
The most common example would be automated vehicles. An automated car is able to distinguish between people walking on the road, poles, traffic signals, and signboards by understanding the data it receives. How does it manage to detect things? 
Unlike traditional learning, deep learning is an artificial neural network that uses many layers to form features from the data it acquires. This data includes image, text, and sound which helps the machine form a clearer picture of the object to easily detect it.  Does this remind you of something? Yes, that’s exactly how our brain works! However, we naturally possess a neural network. 

Why is deep learning so much in demand today? 
As we move to an era that demands a higher level of data processing, deep learning justifies its existence for the world. 
One major defining moment for it would be the use of artificial neural networks which bring out the best outcome. Unlike machine learning, there is no need to build new features and algorithms because deep learning directly identifies features from the data. It uses 150 layers of information to process features directly from the data received and also monitor its own performance. 
Companies that are investing in deep learning are primarily looking to solve complex problems and this form of learning accomplishes that with its collection of large data sets. 
Another reason deep learning thrives in the world today is that it powers the functions that need voice and image detection. Companies that require data on face recognition, object identification, voice-to-speech application, and translation can make optimal use of deep learning techniques. 
Concluding…
While we are not exempted from the fact that deep learning works best only with a huge amount of data and takes time to be set up, there is hope for better performance. 
Moving from a structured and fixed architecture to an ever-evolving one, the next few years will see a rise in businesses moving to this new form of machine learning. Based on the existing data and examples of success, there seems to be an indication that companies using deep learning techniques perform better than ones that don’t. 

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Predicting shrimp diseases with machine learning technology- Tempemail – Blog – 10 minute

Professor Kenton Morgan has joined hands with Aquaconnect, IDH, The Sustainable Trade Initiative and for a project to predict shrimp diseases by using machine learning. The project aims to benefit 13.000 ha of farm area in India in the next two years.
Shrimp diseases cause significant economic losses. The Asian shrimp industry alone reported to have lost at least US$20 billion in a decade. Diseases in the State of Andhra Pradesh that constitutes of nearly 70 per cent of Indian shrimp production are expected to cut down production by 40 per cent this year. Diseases have economic, social and environmental consequences as natural resources such as shrimp feed etc are wasted.
Aquaconnect helps Indian shrimp farmers to manage their farm operations efficiently and improving productivity by using artificial intelligence. Aquaconnect’s FarmMOJO mobile app uses machine learning technology to analyse feed and growth patterns in relation to animal health. The app provides insights to the farmers and suggests appropriate advice for better disease management.
Rajamanohar, CEO, Aquaconnect said, “The Government of India is ramping up support for the aquaculture sector, launching the Pradhan Mantri Matsya Sampada Yojana (PMMSY) to catalyse Blue Revolution 2.0. We are excited to partner with IDH and Prof. Kenton Morgan on pioneering the machine learning efforts in shrimp epidemiology studies to help the Indian farmers to mitigate the risks due to diseases and support long term sustainability of this Industry “
Aquaconnect aims to enhance FarmMOJO’s disease prediction model by using Aquatic Epidemiology, provided by Prof. Kenton Morgan. Professor Kenton Morgan, University of Liverpool has over 40 years of experience in epidemiology. The IDH Aquaculture Program aims to link aquatic epidemiology to the sector. IDH supports and pilots the linkage in several geographies as to tackle feed and disease issues.
Flavio Corsin, Program Director – Aquaculture at IDH, Sustainable Trade Initiative and a trained aquatic epidemiologist said, ‘’Shrimp farming practices and environment change continuously, and these changes can affect disease occurrence in complex and unpredictable ways. Integrating epidemiological tools into Aquaconnect’s farm management app will allow farmers to benefit from Artificial Intelligence in their efforts to understand, predict and prevent diseases’’.

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Explorium reveals $19.1M in total funding for machine learning data discovery platform – gpgmail


Explorium, a data discovery platform for machine learning models, received a couple of unannounced funding rounds over the last year — a $3.6 million seed round last September and a $15.5 million Series A round in March. Today, it made both of these rounds public.

The seed round was led by Emerge with participation of F2 Capital. The Series A was led by Zeev Ventures with participation from the seed investors. The total raised is $19.1 million.

The company founders, who have a data science background, found that it was problematic to find the right data to build a machine learning model. Like most good startup founders confronted with a problem, they decided to solve it themselves by building a data discovery platform for data scientists.

CEO and co-founder, Maor Shlomo says that the company wanted to focus on the quality of the data because not much work has been done there. “A lot of work has been invested on the algorithmic part of machine learning, but the algorithms themselves have very much become commodities. The challenge now is really finding the right data to feed into those algorithms,” Sholmo told gpgmail.

It’s a hard problem to solve, so they built a kind of search engine that can go out and find the best data wherever it happens to live, whether it’s internally or in an open data set, public data or premium databases. The company has partnered with thousands of data sources, according to Schlomo, to help data scientist customers find the best data for their particular model.

“We developed a new type of search engine that’s capable of looking at the customers data, connecting and enriching it with literally thousands of data sources, while automatically selecting what are the best pieces of data, and what are the best variables or features, which could actually generate the best performing machine learning model,” he explained.

Shlomo sees a big role for partnerships, whether that involves data sources or consulting firms, who can help push Explorium into more companies.

Explorium has 63 employees spread across offices in Tel Aviv, Kiev and San Francisco. It’s still early days, but Sholmo reports “tens of customers.” As more customers try to bring data science to their companies, especially with a shortage of data scientists, having a tool like Explorium could help fill that gap.


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Reliability concerns raised over pi-top’s STEM learning laptop – gpgmail


gpgmail has learned of a safety issue and a number of product reliability questions being raised about a modular computer made by a London edtech startup that’s intended for children to learn coding and electronics.

The product, called the pi-top 3, is a Raspberry Pi-powered laptop with a keyboard that slides out to access a rail for breadboarding electronics.

A student at a US school had to be attended by a nurse after touching a component in the device which had overheated, leaving them with redness to their finger.

A spokesperson for Cornell Tech confirmed the incident to us — which they said had happened in June. We’ve withheld the name of the school at their request.

In an internal pi-top email regarding this incident, which we’ve also reviewed, it describes the student being left with “a very nasty finger burn”.

Cornell Tech’s spokesperson told us it has stopped using the pi-top 3 — partly in response to this incident but also because of wider reliability issues with the device. They said some of their grad students will be working on a project with the K-12 team next semester with the aim of creating an alternative that’s more reliable, affordable and safe.

We have also been told of concerns about wider reliability issues with the pi-top 3 by a number of other sources.

We asked pi-top for comment on the safety incident at Cornell Tech and for details of how it responded. The company provided us with a statement in which it claims: “pitop incorporates all possible safeguards into our products to ensure they are safe.”

“As soon as we became aware of this incident we immediately investigated what had happened,” it went on. “We discovered that the incident was a one-in-a-million occurrence. The user dropped a piece of metal, with a specific size and shape, under the unit. This fell in such a way that it touched a particular pin and caused a linear regulator to heat up. They received a small minor burn to the tip of one finger when they tried to recover that piece of metal.”

“This is the only reported incident where a user has been hurt whilst using one of our products,” pi-top added.

It is not clear how many pi-top 3 laptops have been sold to schools at this stage because pi-top does not break out sales per product. Instead it provided us with a figure for the total number of devices sold since it was founded in 2014 — saying this amounts to “more than 200,000 devices in 4 years which have been used by more than half a million people”.

pi-top also says it has sold products to schools in 70 countries, saying “thousands” of schools have engaged with its products. (The bright green color of the laptop is easy to spot in promotional photos for school STEM programs and summer camps.)

The London-based DIY hardware startup began life around five years ago offering a ‘3D-print it yourself‘ laptop for makers via the Kickstarter crowdfunding platform before shifting its focus to the educational market — tapping into the momentum around STEM education that’s seen a plethora of ‘learn to code’ toys unboxed in recent years.

pi-top has raised more than $20M in VC funding to date and now sells a number of learning devices and plug-in components intended for schools to teach STEM — all of which build on the Raspberry Pi microprocessor.

pi-top adds its own layer of software to the Pi as well as hardware additions intended to expand the learning utility (such as a speaker for the pi-top 3 and an “inventors kit” with several electronics projects, including one that lets kids build and program a robot).

The pi-top 3 — its third device — was launched in October 2017, priced between $285-$320 per laptop (without or with a Raspberry Pi 3).

The distinctively bright green laptop is intended for use by students as young as eight years old.

Unusual failure mode

In the internal email discussing the “Cornell failure diagnosis” — which is dated July 16 — pi-top’s head of support and customer success, Preya Wylie, conveys the assessment of its VP of technology, Wil Bennett, that the “unusual failure mode was likely caused by an electrical short on the male 34-pin connector on the underside of the protoboard”.

She goes on to specify that the short would have been caused by the metal SD-card removal tool that’s bundled with the product — noting this was “reported to have been somewhere underneath the protoboard at the time”.

“[Bennett] has recreated the same conditions on his bench in China and has seen the pi-top enter similar failure modes, with an electrical short and subsequent overheating,” she writes.

An additional complication discussed in the email is that the component is designed to stay on at all times in order that the pi-top can respond to the power button being pressed when the unit is off. Wylie writes that this means, if shorted, the component remains “very hot” even when the pi-top has been shut down and unplugged — as heat is generated by the pi-top continuing to draw power from the battery.

Only once the battery has fully depleted will the component be able to cool down.

In the email — which was sent to pi-top’s founder and CEO Jesse Lozano and COO Paul Callaghan — she goes on to include a list of four “initial recommendations to ensure this does not happen again”, including that the company should inform teachers to remove the SD-card removal tool from all pi-top 3 laptops and to remove the SD card themselves rather than letting students do it; as well as advising teachers/users to turn the device off if they suspect something has got lost under the protoboard.

Another recommendation listed in the email is the possibility of creating a “simple plastic cover to go over the hub” to prevent the risk of users’ fingers coming into contact with hot components.

A final suggestion is a small modification to the board to cut off one of the pins to “greatly reduce the chance of this happening again”.

We asked pi-top to confirm what steps it has taken to mitigate the risk of pitop 3 components overheating and posing a safety risk via the same sort of shorting failure experienced by Cornell Tech — and to confirm whether it has informed existing users of the risk from this failure mode.

An internal pi-top sales document that we’ve also reviewed discusses a ‘back to school’ sales campaign — detailing a plan to use discounts to “dissolve as much pi-top [3] stock as we can over the next 8 weeks”.

This document says US schools will be targeted from mid August; UK schools/educators from early September; and International Schools Groups from early September. It also includes a strategy to go direct to US Private and Charter Schools — on account of “shorter decision making timelines and less seasonal budgets”.

It’s not clear if the document pre-dates the Cornell incident.

In response to our questions, pi-top told us it is now writing to pi-top 3 customers, suggesting it is acting on some of the initial recommendations set out in Wylie’s July 16 email after we raised concerns.

In a statement the company said: “Whilst it is highly unlikely that this would occur again, we are writing to customers to advise them to take a common-sense approach and switch off the unit if something has got lost inside it.  We are also advising customers to remove the SD card tool from the unit. These simple actions will make the remote possibility of a recurrence even less likely.”

In parallel, we have heard additional concerns about the wider reliability of the pi-top 3 product — in addition to the shorting incident experienced by Cornell.

One source, who identified themselves as a former pi-top employee, told us that a number of schools have experienced reliability issues with the device. One of the schools named, East Penn School District in the US, confirmed it had experienced problems with the model — telling us it had to return an entire order of 40 of the pi-top 3 laptops after experiencing “a large volume of issues”.

“We had initially purchased 40 pi-tops for middle level computers classes,” assistant superintendent Laura Witman told us. “I met one of the owners, Jesse, at a STEM conference. Conceptually the devices had promise, but functionally we experienced a large volume of issues. The company tried to remedy the situation and in the end refunded our monies. I would say it was learning experience for both our district and the company, but I appreciate how they handled things in the end.”

Witman did not recall any problems with pi-top 3 components overheating.

A US-based STEM summer camp provider that we also contacted to confirm whether it had experienced issues with the pi-top 3 — a device which features prominently in promotional materials for its program — declined to comment. A spokesman for iD Tech’s program told us he was not allowed to talk about the matter.

A separate source familiar with the pi-top 3 also told us the product has suffered from software reliability issues, including crashes and using a lot of processor power, as well as hardware problems related to its battery losing power quickly and/or not charging. This source, who was speaking on condition of anonymity, said they were not aware of any issues related to overheating.

Asked to respond to wider concerns about the pi-top 3’s reliability, pi-top sent us this statement:

pitop is a growing and dynamic company developing DIY computing tools which we believe can change the world for the better. In the past four and a half years we have shipped hundreds of thousands of products across our entire product range, and pitop hardware and software have become trusted assets to teachers and students in classrooms from America to Zimbabwe. pitop products are hard at work even in challenging environments such as the UN’s Kakuma refugee camp in Northern Kenya.

At the heart of our products is the idea that young makers can get inside our computers, learn how they work and build new and invaluable skills for the future. Part of what makes pitop special, and why kids who’ve never seen inside a computer before think it’s awesome, is that you have to build it yourself straight out of the box and then design, code and make electronic systems with it. We call this learning.

The nature of DIY computing and electronics means that, very occasionally, things can fail. If they do, pitop’s modular nature means they can be easily replaced. If customers encounter any issues with any of our products our excellent customer support team are always ready to help.

It is important to say that all electronic systems generate heat and Raspberry Pi is no exception. However, at pitop we do the very best to mitigate thanks to the cutting-edge design of our hardware. Faults on any of our products fall well below accepted thresholds. Although we are proud of this fact, this doesn’t make us complacent and we continually strive to do things better and provide our customers with world-class products that don’t compromise on safety.

Thousands of schools around the world recognise the fantastic benefits the pitop [3], pitop CEED, and pitop [1] brings as a Raspberry Pi-powered device. Our new flagship products, the pitop [4] and our learning platform, pitop Further, take coding education to the next level, as a programmable computing module for makers, creators and innovators everywhere. We are proud of our products and the enormous benefits they bring to schools, students and makers around the world.

Internal restructuring

We also recently broke the news that pi-top had laid off a number of staff after losing out on a large education contract. Our sources told us the company is restructuring to implement a new strategy. pi-top confirmed 12 job cuts at that stage. Our sources suggest more cuts are pending.

Some notable names departing pi-top’s payroll in recent weeks are its director of learning and research, William Rankin — formerly a director of learning at Apple — who writes on LinkedIn that he joined pi-top in March 2018 to “develop a constructionist learning framework to support pi-top’s maker computing platform”. Rankin left the business this month, per his LinkedIn profile.

pi-top’s chief education and product officer, Graham Brown-Martin — who joined the business in September 2017, with a remit to lead “learning, product design, brand development and communication strategy” to support growth of its “global education business, community and ecosystem” — also exited recently, leaving last month per his LinkedIn.

In another change this summer pi-top appointed a new executive chairman of its board: Stanley Buchesky, the founder of a US edtech seed fund who previously served in the Trump administration as an interim CFO for the US department for education under secretary of state, Betsy DeVos.

Buchesky’s fund, which is called The EdTech Fund, said it had made an investment in pi-top last month. The size of the investment has not been publicly disclosed.

Buchesky took over the chairman role from pi-top board member and investor Eric Wilkinson: A partner at its Series A investor, Hambro Perks. Wilkinson remains on the pi-top board but no longer as exec chairman.

The job cuts and restructuring could be intended to prepare pi-top for a trade sale to another STEM device maker, according to one of our sources.

Meanwhile pi-top’s latest device, the pi-top 4, represents something of a physical restructuring of its core edtech computing proposition which looks intended to expand the suggestive utility it offers teachers via multiple modular use-cases — from building drones and wheeled robots to enabling sensor-based IoT projects which could check science learning criteria, all powered by pi-top’s encased Raspberry Pi 4.

Out of the box, the pi-top 4 is a computer in a box, not a standalone laptop. (Though pi-top does plan to sell a range of accessories enabling it be plugged in to power a touchscreen tablet or a laptop, and more.)

pi top 4 4

pi-top is in the process of bringing the pi-top 4 to market after raising almost $200,000 on Kickstarter from more than 500 backers. Early backers have been told to expect it to ship in November.

While pi-top’s predecessor product is stuck with the compute power of the last-gen Raspberry Pi 3 (the pi-top 3 cannot be upgraded to the Raspberry Pi 4), the pi-top 4 will have the more powerful Pi 4 as its engine.

However the latter has encountered some heat management issues of its own.

The Raspberry Pi Foundation recently put out a firmware update that’s intended to reduce the microprocessor’s operating temperature after users had complained it ran hot.

Asked whether the Foundation has any advice on encasing the Raspberry Pi 4, in light of the heat issue, founder Eben Upton told us: “Putting the Pi in a case will tend to cause it to idle at a higher temperature than if it is left in the open. This means there’s less temperature ‘in reserve’, so the Pi will throttle more quickly during a period of sustained high-intensity operation.”

“In general, the advice is to choose a case which is appropriate to your use case, and to update firmware frequently to benefit from improvements to idle power consumption as they come through,” he added.

gpgmail’s Steve O’Hear contributed to this report


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