Oct 29, 2020

Nvidia and SoftBank bosses discuss future of AI

AI
Machine Learning
Nvidia
SoftBank
William Smith
2 min
Nvidia CEO Jensen Huang has discussed AI with SoftBank Chairman Masayoshi Son via video at the annual SoftBank World conference
Nvidia CEO Jensen Huang has discussed AI with SoftBank Chairman Masayoshi Son via video at the annual SoftBank World conference...

Nvidia CEO Jensen Huang has discussed AI with SoftBank Chairman Masayoshi Son via video at the annual SoftBank World conference.

The two are industry heavyweights, responsible for the graphical processing units crucial for deep learning in Huang’s case, and bankrolling countless innovative startups via the Vision Fund in Son’s case

The conversation comes off the back of the two conducting a deal for the sale of British chipmaker Arm, which has come under fire and may still be blocked. Criticism of Nvidia’s $40bn purchase from SoftBank has been based around concerns that Arm's status as a British technology powerhouse will be eroded, with the potential for the loss of jobs in the UK, though Nvidia insists this isn’t the case.

Referring to that purchase, Huang said: “The reason why combining Arm and NVIDIA makes so much sense is because we can then bring NVIDIA’s AI to the most popular edge CPU in the world.

“Of course the CPU is fantastic, energy-efficient and it’s improving all the time, thanks to incredible computer scientists building the best CPU in the world,” Huang said. “But the true value of Arm is in the ecosystem of Arm — the 500 companies that use Arm today.”

Son emphasised that one of the strengths of Arm’s systems-on-chips (SOCs) is their ability to communicate with cloud AI to become smarter. He went on to stress the importance of “edge AI”, connected IoT devices that communicate with cloud AI to learn in perpetuity.

The discussion closed with a focus on the democratising effect of AI, which will allow unspecialised individuals to build complex solutions by relying on AI to fill in the gaps.

“You [will] just ask the computer, ‘This is what I want to do, can you give me a solution?,’” said Son. “Then the computer will give us the solution and the tools to make it happen.”

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Jun 15, 2021

The advantages and disadvantages of AI in cloud computing

AI
CloudComputing
Data
ML
3 min
AI is being used in cloud computing, which works by allowing client devices to access data over the internet remotely, but are there pros and cons?

Cloud computing offers businesses more flexibility, agility, and cost savings by hosting data and applications in the cloud. AI capabilities are now combining with cloud computing and helping companies manage their data, look for patterns and insights in information, deliver customer experiences, and optimise workflows.

We take a look at some of the benefits and drawbacks of AI in cloud computing. 
 

The benefits of AI in cloud computing

 

Lower costs

A major advantage of cloud computing is that it eliminates costs related to on-site data centers, such as hardware and maintenance. Those upfront costs can be restrictive with AI projects, but with cloud enterprises you can access these tools for a monthly fee, making research and development related costs more manageable. AI tools can also gain insights from the data and analyse it without human intervention, reducing staff costs.

Deeper insights 

AI is able to identify patterns and trends in large data sets. Using historical data, AI compares it to the most recent data, which provides IT teams with well-informed, data-backed intelligence. AI tools can also perform data analysis fast so enterprises can rapidly and efficiently address customer queries and issues. The observations and valuable advice gained from AI capabilities result in quicker and more accurate results.

Improved data management

AI enables extensive data management, and cloud computing maximises information security, making it possible to deal with massive amounts of data in a programmed manner to analyse them properly, allowing the business to leverage information that has been “mined” and filtered to meet each need. AI can also be used to transfer data between on-premises and cloud environments. 
 

Intelligent automation 

Businesses use AI-driven cloud computing to be more efficient and insight-driven. AI can automate repetitive tasks to boost productivity, and also perform data analysis without any human intervention. IT teams can also use AI to manage and monitor core workflows. IT teams can focus more on strategic operations while AI performs the mundane tasks. 

Increased security 

With businesses deploying more applications in the cloud, security is crucial in order to keep data safe. IT teams can use different AI-powered network security tools which can track network traffic, they can flag issues, such as finding an anomaly. 
 

The drawbacks of AI in cloud computing

 

Data privacy 

 Enterprises need to create privacy policies and secure all data when using AI in cloud computing. AI applications require a large amount of data, which can include consumer and vendor information. While some data can be anonymous and can't be tied to personally identifiable information, knowing who the data belongs to makes it more valuable. When sensitive information is used, data protection and compliance is a major concern.

Connectivity concerns 

IT teams use the internet to send raw data to the cloud service and recover processed data. Poor internet access can hinder the advantages of cloud-based machine learning algorithms, as cloud-based machine learning systems need consistent internet connectivity. 

While processing data in the cloud is quicker than conventional computing, there is a time lag between transmitting data to the cloud and receiving responses. This is a significant issue when using machine learning algorithms for cloud servers, where prediction speed is one of the primary concerns.

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