He noted that Southeast Asia, in particular, was home to a sizeable untapped and fast-growing digital consumer market. "We must brace ourselves for more uncertainties and more challenges down the road," the Singapore minister said.Īmidst this landscape, though, there were opportunities in Singapore and Asia. He said such ties increasingly were driving trade and investments and, if this trend continued for an extended time, the world was becoming "more bifurcated and decoupled". "I think now, increasingly, there is a new logic at work – let us be friends first, before we do business," Wong said, pointing to a shift in geopolitics relations.
AI PRIME LOCAL CLOUD ANDROID
Google deployed a similar strategy with its Android mobile operating system, which is now the world’s largest by market share.He noted that nations in the last couple of decades also mostly operated on an understanding they still could be business partners, even if they were not "friends". Google has made the TensorFlow architecture and its ML libraries open source in order to incentivize adoption of this platform ecosystem and gain market share. The TPU may be powerful, but what may ultimately matter more is the chip’s underlying TensorFlow architecture, the core intellectual property that Google developed. Version 3 of the company’s Cloud TPU contains up to 1,024 component chips, and each is 100 times more powerful than its Edge TPU for end-use devices. Like all AI chips for cloud computing, Google’s TPU is also an ASIC, which means it is custom designed. One example of such an AI chip is Google’s TPU. Source: Forrester Research Forbes Gartner Tata Communications.īased on McKinsey’s forecast, about two-thirds of the increase in AI hardware demand will be for servers at data centers. This innovation is important for AI applications like autonomous driving, where low latency can mean the difference between life and death. If 5G wireless networks live up to their potential, they will provide rapid data transmission with low latency, enabling more AI computations to take place in the cloud.
Repetitive tasks that require processing a lot of data.Ĭloud computing has become even more appealing with the advent of 5G technology. In short, cloud AI can reduce costs and increase efficiency for Time when applied to the initial drug screening. Take more than a decade to complete, but cloud AI can significantly reduce that One example is in the development of new drugs. Of course, cloud computing supports AI applications far beyond playingīoard games. Alpha Go was the result of AI training in the cloud, powered by Google’s own tensor processing unit (TPU) chip (see more details on the TPU below). The enormous potential of cloud computing was on display when Google’s Alpha Go program defeated the leading human Go player in 2016. Parallel, further widening their computational advantage over edge devices. What’s more, cloud computing allows multiple chips to operate in Relative to edge devices, servers are less constrained by physical sizeĪnd energy demands, so they exceed edge AI in terms of cost and computing Handle an enormous load of computing tasks, far beyond anything that a single machine When networked together in a data center, servers have the scale and power to Here’s a close-up of the server market and why it’s important for AI chips.Ī server is simply a device that hosts and processes computer programs. China’s Huawei has forecast that, by 2025, 97% of companies will be using cloud AI.īut “cloud computing” isn’t ethereal-it’s grounded in massive data centers powered by sprawling server farms. According to Forrester Research and International Data Corporation, the global cloud market increased by more than 30-fold between 20.
AI PRIME LOCAL CLOUD DRIVERS
Servers are key demand drivers of AI chips because most training of AI algorithms occurs in the cloud.
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