Yes, it's true: That old AI gaming assistant April Fools' joke that Nvidia made all the way back in 2017 is now reality. And if Nvidia's plans come to fruition, it could make your favorite PC gaming forums and wikis obsolete. During its Computex keynote, Nvidia revealed "Project G-Assist" for the creation of customizable AI assistants for PC gamers, running on GeForce graphics cards. These AI assistants can be tuned to help you in specific games, providing detailed information about your personal adventures while reacting to on-screen events. They can also analyze and tune your entire gaming PC on the fly.

For years, AMD and especially Intel have dominated the PC industry, even after Qualcomm introduced its first Snapdragon processors for laptops. Now, Microsoft may be giving pride of place to Qualcomm's Snapdragon X Elite processor with an upcoming Windows feature -- and leaving AMD and Intel out in the cold. In May, Microsoft will host its annual Build conference in Seattle. Microsoft will open that conference with a presentation from chief executive Satya Nadella to "share our AI vision across hardware and software," according to Microsoft. That presentation is expected to introduce the Surface Pro 10 and Surface Laptop 5 for consumers, both featuring Qualcomm's surprisingly powerful Snapdragon X Elite processor, based on the Arm architecture.

The costumed children celebrating Halloween with President Biden weren't there for the unveiling of a sweeping new executive order on artificial intelligence. Yet as the US government digests its lengthy, new to-do list and Vice President Kamala Harris heads to a UK summit on AI to sell the president's vision, leaders in Congress and nations around the world may be asking themselves, trick or treat? While this White House is bullish on the power of the president's pen, executive orders have limited power domestically--and none overseas. Behind the White House's rosy PR push about setting a new course for AI lurk the scary but very real monsters of congressional dysfunction and international rivals. Without overcoming both, Biden's AI vision could struggle to take root as his administration hopes it will.

Earlier this month, the European Commission announced it is launching an anti-subsidy investigation into electric vehicles coming from China. The move has long been in the making. The rapid recent growth in popularity of Chinese-made electric vehicles in Europe has raised alarms for the domestic automobile industry on the continent. No matter how it shakes out, an official inquiry could hurt the expansion of the Chinese EV business at a critical moment. Computer vision systems are everywhere.

AI vision, such as image processing with artificial intelligence, is a heavily debated subject. However, the promise of innovative, new technology has not yet materialized in many areas, such as industrial applications. Therefore, as of yet, there are no long-term empirical values for AI vision. Even though there are several embedded vision systems on the market that make it possible to use AI in industrial settings, many facility managers are still hesitant to upgrade their applications and buy one of these platforms. In situations where rule-based image processing has run out of options or has generally failed to find a solution, AI has already shown creative possibilities. Therefore, the question remains as to what is stopping the widespread uptake of this technology.

Developing and deploying vision AI applications is complex and expensive. Organizations need data scientists and machine learning engineers to build training and inference pipelines based on unstructured data such as images and videos. With the acute shortage of skilled machine learning engineers, building and integrating intelligent vision AI applications has become expensive for enterprises. On the other hand, companies such as Google, Intel, Meta, Microsoft, NVIDIA, and OpenAI are making pre-trained models available to customers. Pre-trained models like face detection, emotion detection, pose detection, and vehicle detection are openly available to developers to build intelligent vision-based applications.

Artificial intelligence (AI) plays a key role in the digital age. Self-learning algorithms have the potential to improve processes and be used in production, laboratory analysis and diagnostics. This is also because classical image processing solutions work with a fixed set of rules, making varying or rapidly changing objects a major challenge. Artificial intelligence, on the other hand, can handle such cases effortlessly. So where are the challenges to technology?

Artificial intelligence (AI) is a term you must have heard, even if you are from the IT world. AI is when machines and computer systems simulate human intelligence processes. Right now, AI is literally taking over the world – at least 90% of tech giants invest in it. According to the Data and AI Leadership Executive Survey, the number of AI-friendly companies participating in the survey has doubled in one year. Another survey states that half of the interviewed companies use AI. Some more specific applications of AI include expert systems, natural language processing, speech recognition, and machine (computer) vision. The latter type of AI – computer vision – has already been integrated into road traffic, bank payments, and social networks. For the last decades, AI vision has learned to solve many tasks with an accuracy reaching the human one. "As many others have noticed and pointed out, the neocortex has a highly uniform architecture too across all of its input modalities. Perhaps nature has stumbled by a very similar powerful architecture and replicated it in a similar fashion, varying only some of the details. This consolidation in architecture will in turn focus and concentrate software, hardware, and infrastructure, further speeding up progress across AI. […] Anyway, exciting times." Many companies have started using computer vision in artificial intelligence tasks.

The headlines read "Artificial Intelligence (AI) will completely transform your business." But does the hype match the reality? We have been seeing these exclamations for two decades, but where are the examples? Where are the success stories? Is AI really a game changer, and does it actually apply to my business?

Artificial intelligence (AI) is revolutionizing the way organizations will operate in the future. It is a cornerstone of the digital transformation and will significantly change many business areas we know today. Based on a recent study conducted by Accenture, three quarters of nearly 1500 interviewed C-level executives are afraid of going out of business unless they scale AI [1]. Companies around the globe face the challenge to successfully anchor and spread AI technology in their organization in a value-adding manner. Creating value from AI not only requires operational measures such as technology and infrastructure ramp-up.