nvidia

NVIDIA unveils new GeForce GPUs and AI tools 

by News

NVIDIA’s latest release at CES, featuring the GeForce RTX SUPER desktop GPUs and the RTX 40 SUPER Series graphics cards, marks a notable step up from its previous models. Here’s a straightforward comparison:

  • The new GeForce RTX 4080 SUPER is significantly faster than its predecessor, the RTX 3080 Ti GPU, especially in AI video and image processing. It’s 1.5 times faster for video and 1.7 times faster for images.
  • These latest GPUs are much more powerful when it comes to AI tasks. They offer an AI performance that’s 20 to 60 times better than what you’d get with the older neural processing units. 
  • They’re also embedding these powerful GPUs in laptops from big names like Acer, ASUS, Dell, and others. This means more people can access this high-level AI performance.
  • Beyond the hardware, NVIDIA’s software tools like the TensorRT acceleration for the Stable Diffusion XL model and the AI Workbench toolkit give developers more accessibility to work with generative AI applications and PC model development.
  • Finally, the TensorRT-LLM for Windows now supports more models for PCs, making NVIDIA’s offerings even more attractive. The latest update includes the Phi-2 model, which runs up to 5 times faster than other backends.

These newly introduced GPUs and graphics cards are designed to make AI applications work better on computers and other local devices.

NVIDIA is also working to simplify the use of Large Language Models (LLMs) like chatbots on personal computers. This is great news for businesses and individuals interested in AI chatbots. With NVIDIA’s technology, you can run these chatbots directly on your PC, avoiding cloud services. This not only saves money but also ensures faster response times and better data privacy.

Jensen Huang, NVIDIA’s CEO, said they are getting ready to release the AI Workbench, a tool to help developers make AI projects more easily. They’re also improving gaming with tools like RTX Remix and NVIDIA Avatar Cloud Engine (ACE) to update old games and create digital avatars.

He further unveiled that NVIDIA is also working on making text-to-image AI better with something called the NVIDIA TensorRT™ acceleration of the Stable Diffusion XL model. They’re making tools that help both developers and regular users. 

NVIDIA is also working to simplify the use of Large Language Models (LLMs) like chatbots on personal computers. This is great news for businesses and individuals interested in AI chatbots. With NVIDIA’s technology, you can run these chatbots directly on your PC, avoiding cloud services. This not only saves money but also ensures faster response times and better data privacy.

NVIDIA’s new AI technology, announced at CES, offers cost-saving benefits and has practical applications across various industries like finance, customer support, and document scanning.

In finance, the technology can analyse market trends and manage risk, reducing the need for expensive financial analysts. It can quickly process large volumes of data, identifying patterns that help in decision-making.

For customer support, this AI can power chatbots that handle queries efficiently, cutting down on staffing costs. These chatbots can provide instant responses, improving customer satisfaction while reducing the workload on human employees.

In document scanning, the AI’s ability to process and analyze large amounts of text swiftly makes it ideal for digitizing records. This speeds up data retrieval and reduces the costs associated with manual data entry and physical storage.

Overall, these advancements provide cost-effective solutions, enhancing efficiency and accuracy in these sectors.  The implications of these advancements extend beyond NVIDIA’s product line, indicating a broader trend in the tech industry towards more powerful, efficient, and user-friendly AI applications in everyday technology.

Sunny Kumar(sunny@kaliper.io)

Member of AI Research Team at Kaliper

Business Intelligence Services

Europe Sets a New Standard in AI Regulation: An Overview of the Landmark AI Act Agreement

by News

After an intense three-day negotiation marathon, a milestone in AI regulation has been achieved. The Council presidency and European Parliament representatives have provisionally agreed on the Artificial Intelligence Act, a pioneering proposal that harmonizes AI rules across the EU. This Act is more than a regulatory framework; it’s a commitment to safety, respect for fundamental rights, and adherence to EU values for all AI systems operating within the European market.

The AI Act: A Game-Changer for Europe’s Digital Future

This flagship legislative initiative is poised to revolutionize the AI landscape in Europe. Its core philosophy? A risk-based regulatory approach. The idea is simple but powerful: the greater the potential harm an AI system could cause, the stricter the regulations. This approach places Europe at the forefront of global AI governance, potentially setting a worldwide standard much like the GDPR did for data protection.

Key Innovations in the Provisional Agreement

The provisional agreement brings several significant updates to the table:

New Rules for AI: The agreement introduces regulations for high-impact general-purpose AI models and high-risk AI systems, anticipating future systemic risks.

Enhanced Governance: A revised governance system strengthens enforcement powers at the EU level.

Expanded Prohibitions with Exceptions: While extending the list of prohibited AI uses, the agreement allows for the controlled use of remote biometric identification by law enforcement in public spaces.

Strengthened Rights Protection: Deployers of high-risk AI systems are now obligated to conduct a fundamental rights impact assessment before usage.

Clarifications and Classifications

The agreement refines the definition of an AI system, aligning it with OECD standards. It also limits the Act’s scope, exempting systems used for military, defense, research, and non-professional purposes.

A new classification system is set for AI systems, ensuring that low-risk systems face minimal obligations while high-risk systems must meet more stringent requirements. This balance is crucial in fostering innovation without compromising safety and rights.

Special Provisions for Law Enforcement

Recognizing the unique needs of law enforcement, the agreement includes provisions for the emergency deployment of high-risk AI tools, with necessary safeguards to protect fundamental rights.

Innovations in Governance and Penalties

A new AI Office within the Commission will oversee advanced AI models, while the AI Board, composed of member states’ representatives, will provide crucial coordination and advisory roles. Penalties for non-compliance are proportionate yet substantial, ensuring firms adhere to the regulations.

Supporting Innovation

The agreement promotes innovation-friendly conditions, including AI regulatory sandboxes for real-world testing. Special considerations are given to small businesses, reducing administrative burdens and offering specific derogations.

What Comes Next?

With this provisional agreement in place, technical details will be finalized in the coming weeks. Member states’ endorsement and formal adoption by co-legislators are the next steps, marking the beginning of a new era in AI regulation in Europe.

A/B Testing Analytics

Data Monitoring (chat GPT)

by with No Comment main-blog

Data monitoring is a critical aspect of any analytics program. Without it, you risk missing important insights, encountering data quality issues, and making critical business decisions based on inaccurate information. In this blog post, we’ll explore the importance of data monitoring in analytics, including what it is, why it’s important, and how to do it effectively.

What is data monitoring?

Data monitoring refers to the ongoing process of reviewing, analyzing, and interpreting data to ensure that it is accurate, timely, and relevant. It involves tracking key performance indicators (KPIs), identifying patterns and trends, and evaluating data quality to ensure that it is reliable and trustworthy. Data monitoring can be done manually or with the help of software tools and can be performed at various intervals, depending on the needs of the organization.


Why is data monitoring important in analytics?

  • Ensuring data accuracy: Data monitoring helps to ensure that the data you’re using for analysis is accurate and up-to-date. By regularly reviewing your data, you can identify and correct any errors or discrepancies that may arise, ensuring that your analysis is based on accurate information.
  • Detecting data quality issues: Data monitoring can help you identify data quality issues, such as missing or incomplete data, duplicate records, or inconsistent data. By addressing these issues early on, you can prevent them from impacting your analysis and the decisions you make based on that analysis.
  • Identifying trends and patterns: Data monitoring can help you identify trends and patterns in your data, providing valuable insights into how your business is performing. By tracking KPIs over time, you can identify areas where your business is performing well and areas where it may need improvement.
  • Supporting informed decision-making: By providing accurate and timely data, data monitoring supports informed decision-making. It enables you to make data-driven decisions, based on reliable information, rather than relying on gut instinct or guesswork.


How to do data monitoring effectively?

Effective data monitoring requires a well-defined process and the right tools. Here are some tips for doing data monitoring effectively:

  • Define your KPIs: Identify the KPIs that are most important to your business and track them regularly.
  • Establish a regular monitoring schedule: Determine how often you need to monitor your data to ensure that it remains accurate and relevant. This will depend on the nature of your business and the data you’re tracking.
  • Use the right tools: Use software tools to automate the monitoring process wherever possible. This can save time and reduce the risk of human error.
  • Establish clear roles and responsibilities: Assign roles and responsibilities for data monitoring to ensure that everyone knows what they need to do and when.
  • Address issues promptly: If you identify any issues or discrepancies, address them promptly to ensure that your data remains accurate and trustworthy.

In conclusion, data monitoring is a critical component of any analytics program. By regularly reviewing your data, you can ensure that it remains accurate, relevant, and trustworthy, and that your analysis is based on reliable information. With the right tools and processes in place, you can leverage data monitoring to gain valuable insights into your business and make informed decisions that drive success.