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Cybersecurity startup CybSafe, a ‘behavioral security’ platform, has raised $7.9 million in a Series A funding round led by IQ Capital with participation from Hanover Digital Investments (HDI) GmbH and B8 Ventures. The investment round will be used to focus on expanding its enterprise and mid-market client base. CybSafe is a SaaS product with a per-user-based, subscription licensing model.
As most people in tech know, the big concern with security isn’t so much the system as the people. Indeed, 90% of UK data breaches are generally due to human error, for instance.
CybSafe’s ‘behavior-led’ platform manages these people-related security risks using behavioral science and data analytics by delivering personalized cyber support for users. The company already has 350 clients in 15 countries, including Credit Suisse, Air Canada, HSBC and NHS Trusts.
Launched in 2017, the company was founded by cyber resilience and intelligence expert, Oz Alashe MBE, a former Lieutenant Colonel in the British Army and UK Special Forces who was the first black officer to serve in the Parachute Regiment and UK Special Forces. The team includes former UK Government cybersecurity specialists, behavioral scientists, data scientists, and software engineers.
CybSafe
Alashe says the platform helps users develop security skills and habits through “accredited microlearning content, adaptive support material, and personalized nudges. The combination of contextualized, just-in-time, and on-demand content ensures people get the help they need. They get it whenever they need it. And they get it in the way that is most likely to help them.” The platform is available in 9 different languages.
CybSafe is delivered via a mobile application or within a browser, showing ‘bite-sized’ guidance, alerts, and notifications.
CybSafe’s competitors include KnowBe4, Wombat Security and Infosec, but Alashe says CybSafe’s strength is that it addresses security awareness for the average person: “Security awareness is dead. It’s ineffective. It’s inefficient. And it’s broken. CybSafe uses behavioral science and data analysis to deliver security behavior interventions that work. It has efficacy rates of over 90% for some behaviors to prove it.”
The team includes Jonathan Webster, CTO who previously worked on Her Majesty’s Government Digital Services; Dr John Blythe, Head of Behavioural Science, and a Chartered Psychologist with the British Psychological Society.
New numbers from Canalys show a slowing in the major smartphone decline we saw for 2020. The past year was, of course, a major blow to an industry already suffering a slide. Hope that the arrival of 5G would right the ship were dashed by Covid-19.
Things are looking up, fueled in large part by a killer quarter for Apple. The company posted its earnings last night, putting much of its success at the feet of the iPhone 12. In spite (or perhaps because) of pandemic-fueled delays, the handset arrived in a perfect storm – the beginnings of a “supercycle” that see customers upgrading devices in a kind of critical mass.
Numbers are still down for the fourth quarter of 2020 – but they’re down by only 2% per the firm. That’s due in no small part to what amounted to the iPhone’s best quarter, as the company introduced four 5G-sporting handsets. Canalys shows a 4% increase for Apple, as the device arrived to a wider 5G rollout just in time for the holiday season.
The company snagged the global number one spot, with Samsung taking number two in spite of a 12% decline. Chinese manufacturers Xiaomi, Oppo and Vivo rounded out the top five, all seeing double digit increases, y-o-y.
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The category is expected to see a rebound this year, after suffering declines due first to supply chain concerns and then larger economic issues, stemming from the pandemic.
“The introduction of COVID-19 vaccines is also boosting business confidence for 2021, allowing them to plan and invest,” analyst Ben Stanton says of the figures. “Going forwards, there will be obvious economic ripple effects as government stimulus fades, and there are ongoing concerns around new virus strains. Overall though, sentiment in the industry is positive, and 2021 will see the smartphone market rebound after a 7% decline in 2020.”
Another report from Canalys notes more positive news for the PC market, showing a 35% y-o-y increase, courtesy of tablet and Chromebook sales.
General Motors has announced plans to be carbon neutral by 2040 — removing emissions from all of its products and global operations or offsetting those emissions through carbon credits or carbon capture within the next two decades.
The company also committed to have a fully electric fleet of vehicles by 2035.
It’s a big step for a company whose products are responsible for a large percentage of the greenhouse gas emissions that contribute to global climate change and comes on the heels of a pledge to launch a massive fleet of new electric vehicles and a $27 billion commitment to electrification late last year.
The auto giant said that it would work with the Environmental Defense Fun on its vision for an all-electric future and will work toward eliminating tailpipe emissions from light-duty vehicles by 2035.
GM’s also getting into the charging business too. The company said it would work with “governments, partners, and suppliers around the world too build out the necessary charging infrastructure and encourage the use of renewable energy n electric vehicle charging.”
To power the company’s operations, GM said it will use only renewable energy power at its U.S. facilities by 2030 and by 2035 all of its operations worldwide will use renewable power.
These commitments are also going to extend to its supply chain over time as the company works with its suppliers to reduce their emissions, increase transparency and source sustainable materials.
“While electric vehicles do not produce tailpipe emissions, it is critical that the impact associated with production and charging is incorporated in our plans. By working with utility companies to provide access to more renewable energy sources, GM hopes to address the entire production cycle of future EVs, with benefits that will extend far beyond our own vehicles and operations,” the company’s chief executive Mary Barra said in a statement.
This commitment is going to require a massive transformation that encompasses more than GM alone, Barra wrote, because “making the transition to an EV is simply not possible right now – either because the appropriate vehicles do not exist or because access to charging is limited where [people] live and work.”
Storetasker is an online marketplace focused on connecting Shopify merchants with developers and other experts who can help grow their business.
The product is now owned by the startup previously known as Lorem. Co-founder and COO Charlie Fogarty explained that while Lorem originally had a broader mission of connecting small businesses and developers, “We realized that Shopify and e-commerce was by far our best customer segment … so we basically acquired our main competitor, Storetasker, and merged the two business” under the Storetasker name.
The acquisition (which included the Storetasker product and expert network, but not the team) actually took place last year, and Fogarty said, “We’ve spent the last 10 months basically rebuilding the product from the ground up. We’ve taken years of learning and combined it into a rebrand, a new product and a new end-to-end customer experience.”
The core proposition is still the same, however. A Shopify merchant should be able to visit Storetasker, describe their project in simple terms and then within a few hours, Storetasker will match them up with one of the experts in the network, who they can work with directly.
Storetasker has already been used by more than 30,000 brands on Shopify, including Boll & Branch, Chubbies, Aisle, Alpha Industries, Truff Hot Sauce and Branch Furniture. Fogarty said the average project size is just $300 and usually involves adding custom designs and unique features to a Shopify store.
Image Credits: Storetasker
You could use a general marketplace like Upwork or Fiverr to find a freelance developer, but where Storetasker has conducted more than 5,000 interviews to vet its talent and picks the right expert for each customer, Fogarty said that on other platforms, “You have to sift through unvetted talent … The hiring burden is placed on the brand.”
Plus, he noted that brands can use Storetasker for more than development help — they also use it to find experts on conversion and “all the different aspects of e-commerce.”
In addition to the new product, Storetasker is also announcing that it raised $3.2 million in Series A funding last year from Flybridge, Founder Collective, and FJ Labs.
Looking ahead, Fogarty said he sees plenty of room to grow while remaining focused on the Shopify ecosystem. After all, there are more than 1 million stores on the platform, with $200 billion in total sales to date.
Source: https://techcrunch.com/2021/01/28/storetasker-relaunch/
A new type of neural network that’s capable of adapting its underlying behavior after the initial training phase could be the key to big improvements in situations where conditions can change quickly – like autonomous driving, controlling robots, or diagnosing medical conditions. These so-called ‘liquid’ neural networks were devised by MIT Computer Science and Artificial Intelligence Lab’s Ramin Hasani and his team at CSAIL, and they have the potential to greatly expand the flexibility of AI technology after the training phase, when they’re engaged in the actual practical inference work done in the field.
Typically, after the training phase, during which neural network algorithms are provided with a large volume of relevant target data to hone their inference capabilities, and rewarded for correct responses in order to optimize performance, they’re essentially fixed. But Hasani’s team developed a means by which his ‘liquid’ neural net can adapt the parameters for ‘success’ over time in response to new information, which means that if a neural net tasked with perception on a self-driving car goes from clear skies into heavy snow, for instance, it’s better able to deal with the shift in circumstances and maintain a high level of performance.
The main difference in the method introduced by Hasani and his collaborators is that it focuses on time-series adaptability, meaning that rather than being built on training data that is essentially made up of a number of snapshots, or static moments fixed in time, the liquid networks inherently considers time series data – or sequences of images rather than isolated slices.
Because of the way the system is designed, it’s actually also more open to observation and study by researchers, when compared to traditional neural networks. This kind of AI is typically referred to as a ‘black box,’ because while those developing the algorithms know the inputs and the the criteria for determining and encouraging successful behavior, they can’t typically determine what exactly is going on within the neural networks that leads to success. This ‘liquid’ model offers more transparency there, and it’s less costly when it comes to computing because it relies on fewer, but more sophisticated computing nodes.
Meanwhile, performance results indicate that it’s better than other alternatives for accuracy in predicting the future values of known data sets. Th next step for Hasani and his team are to determine how best to make the system even better, and ready it for use in actual practical applications.
