Deploying machine learning is a multi-step process. Deploying machine learning is a multi-step process. It involves selecting a model, training it for a specific task, validating it with test data, and then deploying and monitoring the model in production. Here, we'll discuss these steps and break them down to introduce you to machine learning. Machine learning refers to systems that, without explicit instruction, are capable of learning and improving.

The miriac AIP-S32G274A embedded platform provides customers with a highly efficient way to implement AI into their connected edge appliances. NXP Gold partner MicroSys Electronics' new embedded SoM (system-on-module) platform miriac AIP-S32G274A, which is based on NXP S32G vehicle network processors, now supports Hailo-8 AI accelerator modules. A result of MicroSys's partnership with Hailo, the powerful AI solution delivers up to 52 TOPS (tera operations per second) for ASIL D safe zonal gateways and real-time controls in autonomous vehicle and stationary machine applications. The new, application-ready SoM-based AI platform targets a wide range of industrial and mobility markets such as Industry 4.0 gateways, zonal automotive controllers, ADAS (advanced driver assistance system) controllers, heavy machinery controls, smart farming robots, autonomous logistics vehicles, robots, and more. Powered by the NXP S32G274A vehicle network processor, miriac AIP-S32G274A can integrate up to two advanced Hailo-8 AI accelerators to reach its maximum AI performance of 52 TOPS, providing best-in-class processing performance and deep learning capabilities for decentralized situational awareness.

Take a look at how 5G can push AI to release its full potential and few application scenarios. Field trials are underway, components are coming, and testing covers the spectrum in more ways than one. What are the challenges and how is the ecosystem shaping up? Find out more in this month's In Focus series. The power of AI applications will always depend on the strength of their networks.

Safety advocates call for a national AI testbed, with trust based on'engineering discipline'. The splashy unveiling of Tesla's robot assistant stokes the ongoing debate about AI safety and how automated systems can be tested and validated before they are unleashed on city streets and factory floors. The fear during the initial round of AI hyperbole focused on malevolent, self-replicating, HAL-like machines eventually overpowering their creators or roaming uncontrolled on battlefields. The debate has since become more pragmatic, with a sharper and welcome focus on safety. Specifically, how can we promote AI safety in ways the will allow human operators to trust autonomous systems in applications that for now remain well short of mission critical, requiring 99.999 percent reliability?

The ability to do AI inferencing closer to the end user is opening up a whole new world of markets and applications. While AI originally was targeted for data centers and in the cloud, it has been moving rapidly towards the edge of the network where it is needed to make fast and critical decisions locally and closer to the end user. Sure, training can be still done in the cloud, but in applications such as autonomous driving, it is important that the time-sensitive decision making (spotting a car or pedestrian) is done closer to the end user (the driver). After all, edge systems can make decisions on images coming in at up to 60 frames per second, enabling quick actions. These systems are made possible through edge inference accelerators that have emerged to replace CPUs, GPUs and FPGAs at much higher throughput/$ and throughput/Watt.

So much happened in the auto industry this month it can't fit in one column; I whittled it down to the things that interested me. There is a lot of variety by topic -- from Auto Shanghai to a new operating system to an interesting DARPA story to the European Union proposal for AI use regulation. These ten stories are summarized in the following table. Auto Shanghai Auto Shanghai is among the world's largest auto shows and is the first auto show to be held after the pandemic. The attendance is expected to reach about 1 million people and around 1,000 exhibitors.

Greenwaves reveal their latest AI chip, GAP9. Just like the previous generation, it is aimed at AI inferencing in systems at the very edge of the network. Edge computing will increasingly become an integral part of the digital transformation phenomenon. The main benefits deriving from the use of these technologies are the reduction of processing latency, which allows real-time responses, and the saving of bandwidth, sending already processed and, therefore, smaller information to the data center. Compared to GreenWaves Technologies' currently shipping product, GAP8, the latest GAP9 reduces energy consumption by 5 times while enabling inference on neural networks 10 times larger.

In reality, the most popular methods for assessing the accuracy of an AI model aren't as trustworthy as you might think... You just watched The Shining and your neighborhood's seen an unusual number of burglaries in recent weeks. You're on edge, but then you remember: your new security system. It's state of the art, with a camera that promises to recognize not just strange movements but also a stranger's face, in real-time. You trust that this system will alert you if someone suspicious approaches your home, so you sleep soundly.

Why is self-driving so hard and so complex? Humans have walked on the moon, split the atom and flown faster than the speed of sound, and yet self-driving continues to elude us. Why is self-driving so hard and so complex? Humans have walked on the moon, split the atom and flown faster than the speed of sound. Yet despite the best efforts of our smartest engineers, backed with many billions of dollars from our wisest VCs and promoted with the passion of our most enthusiastic optimists, self-driving continues to elude us.

The 2025 market for AI, including ADAS and robotic vehicles, is estimated at $2.75 billion – of which $2.5 billion will be "ADAS only"... Artificial Intelligence (AI) is gradually invading our lives through everyday objects like smartphones, smart speakers, and surveillance cameras. The hype around AI has led some players to consider it as a secondary objective, more or less difficult to achieve, rather than as a central tool to achieve the real objective: autonomy. Who are the winners and losers in the race for autonomy? "AI is gradually invading our lives and this will be particularly true in the automotive world" asserts Yohann Tschudi, Technology & Market Analyst, Computing & Software at Yole Développement (Yole). "AI could be the central tool to achieve AD, in the meantime some players are afraid of overinflated hype and do not put AI at the center of their AD strategy".