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1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minuteEE Times On Air Episode 53, published September 13, 2019, connected three technology shifts: new 5G smartphone silicon, the growing variety of infrastructure behind AI computing, and the need to make autonomous-vehicle safety a shared, independently assessed baseline. Its chip announcements and market forecasts are historical; they describe the landscape discussed in 2019, not current product specifications or market measurements.
What 5G smartphone chips were announced in 2019?
The episode described operators expanding 5G coverage and argued that broader adoption would require a wider variety of 5G smartphones. Huawei, Samsung, and Qualcomm represented different ways of integrating cellular and computing functions into handset platforms.
| Platform | Approach and stated features in the 2019 episode | Speed figure cited |
|---|---|---|
| Huawei Kirin 990 5G | Huawei described it as the world’s first flagship 5G system-on-chip. The episode cited 10.3 billion transistors, TSMC 7nm+ EUV manufacturing, and support for both non-standalone and standalone 5G radio architectures. | Roughly 2 to 2.5 Gb/s in sub-6GHz 5G, as characterized in the episode; this is a 2019 claim, not a current specification. |
| Samsung Exynos 980 | An 8nm FinFET mobile AI processor with an integrated 5G modem and Wi-Fi 6 support. | Samsung’s cited sub-6GHz peak was 2.55 Gb/s, as reported by EE Times in 2019. |
| Qualcomm Snapdragon 7 Series 5G platform | A modem-RF system approach integrating functionality toward the antenna and bringing 5G to a Snapdragon platform. Qualcomm said 12 leading global OEMs and brands planned to use it, including OPPO, Redmi, Vivo, Motorola, and HMD/Nokia. | Not stated in the EE Times episode (2019). |
The episode said Huawei and Samsung supported 2G, 3G, 4G, and 5G, and combined CPU/GPU resources, neural-processing capability, and image-signal processing. These announcements illustrated different integration strategies, not a present-day ranking: the Kirin and Exynos combined application-processing and modem functions, while Qualcomm emphasized an integrated modem-RF system. The episode’s figures and feature descriptions are specific to 2019.
What are the four types of AI cloud?
In the episode, ABI Research analyst Lian Jye Su divided AI-cloud infrastructure into four segments. The distinction is about where computing infrastructure is deployed, who operates it, and which workloads it serves—not four different kinds of artificial intelligence.
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| Cloud segment | What it means | Examples cited in the episode |
|---|---|---|
| Public cloud | Shared cloud infrastructure operated by large service providers. | Google, Amazon, Microsoft, Alibaba, Baidu, and Tencent. |
| Enterprise data centers / private cloud | Infrastructure kept on an organization’s premises, often where it wants to retain control of data or systems. | Banks, medical institutions, research and development groups, and academia. |
| Hybrid cloud | A combination of public-cloud and private infrastructure. | VMware, Rackspace, and Dell were named as examples in the episode. |
| Telco cloud | Infrastructure deployed by telecommunications providers for network and computing workloads. | Core-network, 5G, IT, and edge workloads. |
Where does edge AI fit?
Edge AI is not a fifth cloud category. It describes processing near the data source or device, rather than sending every task to a remote cloud. The episode’s examples ranged across:
- Factory gateways and manufacturing systems.
- End devices such as smartphones and sensors.
- Automotive systems, robotics, and drones.
- Cameras.
Examples named in the episode included Nvidia Jetson, Intel Movidius Myriad X, and Qualcomm Snapdragon for edge platforms; Apple and Huawei smartphone ASICs; Nvidia and Intel Mobileye automotive implementations; and hardware from cloud providers Amazon, Google, and Microsoft. These examples show why AI computing can involve chips in devices and local systems as well as accelerators in data centers.
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What did the episode forecast for AI chips?
The episode reported ABI Research’s 2019 estimate that cloud providers designing accelerators for their own public-cloud workloads could account for 15–18% of the cloud AI-chip market by 2024. It also cited a possible AI-chip market of as much as $10 billion by 2024, about two-and-a-half times the earlier market size. Both numbers are historical forecasts made in 2019, not measurements of the market in 2024 or today.
At the time of the episode, Nvidia was described as the clear AI-chip market leader, followed by Intel. The main opening for challengers was identified as private enterprise data centers. The logic was that cloud providers could develop hardware for their own workloads, while enterprises had distinct deployment needs and might consider alternatives to the established leaders.
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Should autonomous-car companies compete on safety?
The episode’s argument is no: safety should be a common baseline established through comparable testing and independent assessment, not a proprietary advantage that companies claim by reaching market sooner or disclosing selected private test results. Junko Yoshida criticized the industry’s tendency to keep road-test methods, simulations, test sequences, and safety data confidential as a sign of immaturity.
That argument does not mean developers should stop testing on public roads. Road tests can reveal how a vehicle responds to unexpected events; simulation and closed-course testing remain important parts of the overall process. The proposed remedy was an independent third-party assessor or industry body to define minimum safety behavior and comparable tests, so that claims can be evaluated on a common basis.
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Michael Krutz, then President and Managing Director of Wind River Japan, put the proposed shift this way: “Okay, step number one is stop treating it as an advantage. Step number two is, leverage these other industries that have already figured some of this out for what you can. And that gets you part of the way.”
Four requirements for an autonomous system
- Secure: People cannot harm or compromise the system.
- Safe: The system cannot harm people.
- Reliable: It consistently performs what it was designed to do.
- Certifiable: It can meet government regulations and applicable standards.
Together, these requirements point to the dimensions an assessment needs to address: transparency of testing, independent review, security, functional safety, reliability, and certification. The episode’s central distinction is between competing to build better systems and competing to define whose safety claim the public should trust.
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