May 02, 2022

The Time Travel Method of Debugging Software

By removing the preconceived notions about how challenging programming is, Jason Laster became more confident in building a developer-friendly debug tool. “We want to make software more approachable,” he said. “We want more people to feel like they can program and do things that don’t require a math degree.” He went on to say, “Imagine being a Project Manager and asking your engineer why something broke and receiving a long explanation that still leaves your question unanswered. Using Replay, they can share the URL with the engineers who can just go in and leave a comment. Now, the PM can recognize the function and identify what went wrong on their own. If anybody along the way can record the issue with Replay, then everyone downstream can look at the replay, debug it and see exactly what went wrong.” Acknowledging that it’s easy to mistake Replay as another browser recorder tool, Laster explained how Replay differs. “On one end of the spectrum, you have something like a video recorder, then go along that spectrum a little bit further and you have something like a session replay tool and observability tool.

Software AI Accelerators: AI Performance Boost for Free

The increasing diversity of AI workloads has necessitated a business demand for a variety of AI-optimized hardware architectures. These can be classified into three main categories: AI-accelerated CPU, AI-accelerated GPU, and dedicated hardware AI accelerators. We see multiple examples of all three of these hardware categories in the market today, for example Intel Xeon CPUs with DL Boost, Apple CPUs with Neural Engine, Nvidia GPUs with tensor cores, Google TPUs, AWS Inferentia, Habana Gaudi and many others that are under development by a combination of traditional hardware companies, cloud service providers, and AI startups. While AI hardware has continued to take tremendous strides, the growth rate of AI model complexity far outstrips hardware advancements. About three years ago, a Natural Language AI model like ELMo had ‘just’ 94 million parameters whereas this year, the largest models reached over 1 trillion parameters.?

Cybersecurity in the digital factory for manufacturers

Many companies are extremely hesitant about introducing the Industrial Internet of Things (IIoT) or cloud systems because they believe it will open the door to cybercriminals. What they fail to realize is they’re already facing this danger every day. A simple email with an attachment or a link can result in the encryption of all the information on a server. You’re at risk even if you haven’t implemented an entire ecosystem connecting customers and suppliers. That’s why it’s essential that you’re aware of the threats and be ready to respond quickly in the event of a cyberattack. Cybersecurity is currently on everyone’s lips. In many widely publicized cases, large companies have fallen victim to cyberattacks that compromised their operations in one way or another. In some of these cases, the companies’ security policies had not kept up with the past decade’s rapid changes in the use of digital technologies and tools. They mistakenly thought a cyberattack could only affect others. The sheet metal processing sector is no exception to this reality.

Chaos Engineering and Observability with Visual Metaphors

Monitoring and observability have become one of the most essential capabilities for engineering teams and in general for modern digital enterprises who want to deliver excellence in their solutions. Since there are many reasons to monitor and observe the systems, Google has documented Four Golden Signals or metrics that define what it means for the system to be healthy and that are the foundation for the current state of the observability and monitoring platforms. The four metrics are described below: Latency is the time that a service takes to service a request. It includes HTTP 500 errors triggered due to loss of connection to a database or other critical backend that might not be served very quickly. Latency is a basic metric since a slow error is even worse than a fast error. Traffic is a measure of how much demand is being placed on the system. It determines how much stress is the system taking at a given time from users or transactions running through the service. For a web service, for example, this measurement is usually HTTP requests per second.?

Reimagining the Post Pandemic Future: Leveraging the benefits of Hyperautomation

As the world emerges from the impact of the pandemic, hyperautomation solutions will power digital self-services to take center stage connecting businesses with customers. With customers opening bank accounts remotely, consulting doctors online, interacting with governments via citizen self-serve, and so on, the scope of tech-enabled services keeps expanding from time to time. All this implies that there will be a gradual shift away from the traditional back-office towards self-serve. From a hyperautomation standpoint, this shift will see a considerable boost from low-code platforms with favorable B2C type interactions. Rich and sophisticated user experiences centered around simplicity and ease of use will be in demand. New user experiences will break ground allowing more flexibility and improved speed-to-solution. In addition to B2C type low-code portals, Artificial Intelligence (AI) and analytics will be in demand. For example, organizations will deploy AI technologies heavily to assist customer interactions.?

UK regulators seek input on algorithmic processing and auditing

On the benefits and harms of algorithms, the DRCF identified “six cross-cutting focus areas” for its work going forward: transparency of processing; fairness for those affected; access to information products, services and rights; resilience of infrastructure and systems; individual autonomy for informed decision-making; and healthy competition to promote innovation and better consumer outcomes. On algorithmic auditing, the DRCF said the stakeholders pointed to a number of issues in the current landscape: “First, they suggested that there is lack of effective governance in the auditing ecosystem, including a lack of clarity around the standards that auditors should be auditing against and around what good auditing and outcomes look like. “Second, they told us that it was difficult for some auditors, such as academics or civil society bodies, to access algorithmic systems to scrutinise them effectively. Third, they highlighted that there were insufficient avenues for those impacted by algorithmic processing to seek redress, and that it was important for regulators to ensure action is taken to remedy harms that have been surfaced by audits.”

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