October 19, 2022

Small businesses need more help with tech. Here are five ways to get it

While some small firms might to look to hire an IT director on a temporary basis, McCabe says most will want to avoid bringing in a costly consultant. "There's a sliver of venture capital-backed tech companies with a CIO or someone with an equivalent title, but not the vast majority of small firms," she says. For SMB owners and managers who want technology expertise without high fees, McCabe suggests a different route. "I'm a big fan of industry associations and regional technology councils. They can be really great because people in these organisations are in businesses like yours," she says. ... "The challenge for smaller organisations is developing brand and trust," says Bev White, CEO of Nash Squared. "Where there are so many players, how can you stand out from the crowd when few people might know who you are?" Her firm's research suggests twice as many SMBs (23%) as larger corporates (10%) are extremely or very effective at scaling good ideas and stopping poor ideas quickly. SMBs should be on the lookout for novel ways to source technological solutions to business challenges, so go to conferences, attend meetups and take part in specialist events.

Platform Engineering: What Is It and Who Does It?

One issue when adopting platform engineering is the tendency to build another silo. A good example would be a ticketing system where users can request features or report bugs, the requests go into the platform engineering realm, and are eventually resolved. You can combat this by focusing on enabling users to self-serve their own needs with your portal by providing accurate and relevant documentation, training sessions and pairing with users to solve their problems. Another issue is prioritizing the right things. There are a lot of users from many different parts of your organization, so having a single feature request pipeline for those things users cannot self-serve with a committee deciding on priority is essential to servicing the needs of your organization effectively. Keep your platform team adaptable and not stuck in the past ways of doing things. With the rapid pace of change in IT, it’s hard to keep up. Enablement is one way you can ease the burden on your team, but also allowing your team a consistent amount of time to train on new technologies is another.

Making SBOMs Actionable

There is no doubt that SBOMs should be requested from your software vendors and that you should consider creating SBOMs along with your own developed software. It’s all about the proper storage of the SBOMs so you can be sure they’re recent, searchable and trustworthy and tamper-proof. The benefits and use cases for SBOMs are numerous; they vary across stakeholders who produce, choose and operate software and are amplified when combined. Use cases for SBOMs include better software development, supply chain management, vulnerability management, asset management and high assurance processes. The benefits include reducing cost, mitigating security risk, license risk and compliance risk. But the key is making the SBOM actionable. No developer, no software maintainer or DevOps engineer wants to manually collect the dependencies and produce SBOM documents. It needs to be fully automated within the software build and deployment pipeline and there needs to be a proactive check of where it’s currently running.

How We Built Testability with Psychological Safety

Simply throwing people together and expecting them to figure out how to work together like this will most likely result in failure, but that's the point. You want them to fail, not to stop and go back to how things were, but to figure out why they failed. You want them to talk about what is and isn't working and what they can do differently. The problem leadership needs to help these people overcome is the assumption that high-performing people and teams don't fail. So we will do our best to avoid failure at the first signs of it. Leadership must show that failure is a natural by-product of experimentation and that high performers produce and share their failures; not avoid, deny, ignore or distort them, but learn from them. But for people to embrace failure like this, they need high levels of psychological safety, meaning team members can take interpersonal risks and be vulnerable by sharing what they don't know, what they don't understand or mistakes they have made without fear of judgement or that it will affect their prospects negatively.

?

Announcing open innovations for a new era of systems design

The root of trust is an essential part of future systems. Google has a tradition of making contributions for transparent and best in-class security, including our OpenTitan discrete security solutions on consumer devices. We are looking ahead to future innovations in confidential computing and varied use-cases that require chip-level attestation at the level of a package or System on a Chip (SoC). Together with other industry leaders, AMD, Microsoft, and NVIDIA, we are contributing Caliptra, a re-usable IP block for root of trust measurement, to OCP. In the coming months we will roll out initial code for the community to collectively harden together. ... To address the challenges of reliability at scale, we’ve formed a new server-component resilience workstream at OCP, along with AMD, ARM, Intel, Meta, Microsoft, and NVIDIA. Through this workstream, we’ll develop consistent metrics about silent data errors and corruptions for the broader industry to track. We’ll also contribute test execution frameworks and suites, and provide access to test environments with faulty devices.

Is Reinforcement Learning Still Relevant?

Autonomous machine intelligence is the common goal in both these approaches, but with reinforcement training there is always a human agent driving the working of the machine, while unsupervised learning proposes to learn from observation. Self-supervised learning advocates talk about the inefficiency of trial-and-error methods but uncertainty still remains a major barrier for self-supervised learning. Sergey Levine from Berkeley AI Research recently proposed a solution of combining self-supervised learning with offline-reinforcement learning, that explores the possibility of enabling models to understand the world without supervision and allow reinforcement learning to explore causal understanding of the world, thus expanding the dataset close to infinite. Yann LeCun proposed the World Model in paper in June 2022, which uses a “cost module” in its architecture that measures the energy-cost of an action by the machine. When reinforcement learning is scaled on larger datasets, the reward maximisation also needs further scaling.

Read more here ...