Category: tips and tricks

PostgreSQL continues to solidify its effectiveness as an enterprise-ready database in its 100% free and open-source version. Data teams should feel confident with OS PostgreSQL and not be taken in by less versatile and more costly open-core Postgres repackaging.

That said, backing open-source PostgreSQL with the right supplemental technology strategy can have a profound impact on the value the venerable relational database delivers. For example, enterprises that support their PostgreSQL database implementations with fast storage strategies can realize high-end performance advantages, including substantial increases in the TPS workloads that servers can handle.

The interest and investment in AI is skyrocketing, and generative AI is fueling it. Over one-third of CxOs have reportedly already embraced GenAI in their operations, with nearly half preparing to invest in it. 

What’s powering it all, AI chips used to receive less attention. Up to the moment, OpenAI’s Sam Altman claimed he wants to raise up to $7 trillion for a “wildly ambitious” tech project to boost the world’s chip capacity. Geopolitics and sensationalism aside, however, keeping an eye on AI chips means being aware of today’s blockers and tomorrow’s opportunities.

In the vast realm of artificial intelligence, deep learning has emerged as a game-changer, especially in the field of image recognition. The ability of machines to recognize and categorize images, much like the human brain, has opened up a plethora of opportunities and challenges. Let's delve into the techniques deep learning offers for image recognition and the hurdles that come with them.

Convolutional Neural Networks (CNNs)

Technique: CNNs are the backbone of most modern image recognition systems. They consist of multiple layers of small neuron collections that process portions of the input image, called receptive fields. The results from these collections are then tiled so that they overlap, to obtain a better representation of the original image; this is a distinctive feature of CNNs.

GenAI is everywhere you look, and organizations across industries are putting pressure on their teams to join the race – 77% of business leaders fear they’re already missing out on the benefits of GenAI.

Data teams are scrambling to answer the call. But building a generative AI model that actually drives business value is hard.

The new SingleStoreDB release v8.5 provides several new vector features. In this article, we'll evaluate ANN Index Search with the new VECTOR data type using the Fashion MNIST dataset from Zalando.

The notebook file and SQL code are available on GitHub.

This is part two of my series on the OCP-17. In my first post, I explained why it’s still worthwhile to tackle the trick questions and arcane details of this exam. Each of us has our own sense of how much knowledge is good enough to get a job done, like writing Java code. The examiners at Oracle probably put the bar much higher than you. If you can’t somehow make the journey fun, you’re bound to give up. So, your motivation needs an upgrade, and you must put in the right practice. In this post, we will look at both.

Amateur Versus Professional Practice

Not all practice makes perfect. Poor practice doesn’t even make you proficient. It’s not the hours that count, although Malcolm Gladwell in Outliers claimed you need at least 10,000. This sweeping statement with its conveniently round number is questionable science, to say the least. Excellence itself is not a well-defined notion. People’s innate aptitudes are not created equal. Some are born geniuses. But most importantly, the hours you put in must be effective.

Distributed transactions, also known as XA transactions, emerged to address the complexity of coordinating transactions across multiple databases or systems in a distributed environment. Imagine you’re conducting an orchestra where each musician represents a different database or service. Just like ensuring harmony in music requires precise coordination, maintaining transactional integrity across distributed systems demands careful orchestration.

This is where a two-phase commit (2PC), a vital Aspect of XA transactions, steps in. 2PC acts as the conductor, ensuring that all musicians (or database participants) are ready to commit before the final note is played. Just as every instrument must be in tune before a symphony reaches its crescendo, 2PC ensures that all components of a distributed transaction are in sync before proceeding, thus guaranteeing the integrity of the transaction across the distributed landscape.

The use of AI chatbots in healthcare necessitates a comprehensive approach to address vital considerations. From data training to security measures and ethical practices, a wide range of precautions must be implemented. Human monitoring, user education, and mitigating the risks of anthropomorphism are crucial Aspects to focus on.

Find out how continuous monitoring and feedback promote transparency, user safety, privacy protection, and the provision of reliable information.

What if we wanted to analyze a small piece of text with no additional information or context and be able to get the most reasonable label that we wish to define for our own data? This can feed the more deterministic policy engines and rule engines, and even be a part of a larger context-driven analysis as required. OpenAI does provide a means to "content moderate" with preset classifications that can determine if your text belongs to one or more of the more vile categories. However, this analysis is more about how we can get more custom to defining our own labels against a given sentence or phrase.

We will look at 4 categories: viz. politics, PHI/PII, legal matters, and company performance. Given that we don't have the option of gathering probability scores from Open AI on such custom labels (at this point in time), we will try the more user-oriented prompt engineering route in Option 1 while Option 2 evaluates other pre-trained models from Hugging Face for the same. 

The Trusted Platform Module (TPM) is an important component in modern computing since it provides hardware-based security and enables a variety of security features. TPM chips have grown in relevance in both physical and virtual contexts, where they play a critical role in data security and preserving the integrity of computer systems. TPM chips, their functionality, and how they are used in virtualization technology will be discussed in this article.

Introduction

In today’s computer ecosystem, trusted computing is critical. It is critical to secure system and data security and integrity. TPM chips are a critical component in attaining this aim, and they have far-reaching consequences for virtualization technology.