Apr 15, 2021

Legend: AMD CEO, Lisa Su

AI
machinelearning
Leadership
Strategy
William Smith
2 min
As the President and CEO of US semiconductor giant Advanced Micro Devices (AMD), Lisa Su is well deserving of the title of legend in the field of AI
As the President and CEO of US semiconductor giant Advanced Micro Devices (AMD), Lisa Su is well deserving of the title of legend in the field of AI...

As a manufacturer of graphical processing units (GPUs), AMD plays an important role in the field of machine learning, where, thanks to their highly parallel nature, GPUs are invaluable. Despite this, AMD has not entered the field as fully as its main competitor such as Intel or GPU manufacturer Nvidia, for instance not offering a dedicated AI chip. Under Lisa Su’s leadership, that is changing, however. She has said that the company is considering building dedicated accelerators for applications such as machine learning, adding: “We’ll absolutely see AMD be a large player in AI”. 

Born in Taiwan, Su arrived in the United States at the age of three years old. Later, she studied electrical engineering at the Massachusetts Institute of Technology before going on to work for Texas Instruments and IBM, where she helped propel significant advancements in semiconductor design. She joined AMD as SVP in 2012 from Freescale Semiconductor, where she had served as Chief Technology Officer, before attaining the role of CEO and President in 2014.

In a keynote speech at this year’s Consumer Electronics show, Su emphasised the company’s commitment to digital transformation in society, saying: “AMD is incredibly proud to be at the heart of products, services and experiences keeping us productive, learning, connected and entertained in an increasingly ‘digital first’ world,” said Dr. Su. “We are committed to consistently pushing the envelope on what’s possible in PCs, gaming, data centers and the cloud together with our industry partners.”

The Xilinx purchase

The company recently cemented its position in the semiconductor industry with a purchase of competitor Xilinx in a $35bn deal. Su said of the deal: “Our acquisition of Xilinx marks the next leg in our journey to establish AMD as the industry’s high performance computing leader and partner of choice for the largest and most important technology companies in the world. [...] By combining our world-class engineering teams and deep domain expertise, we will create an industry leader with the vision, talent and scale to define the future of high performance computing.”

As one of the industry’s most prominent women, Su also made our recent list of the Top 100 Women in Technology, coming in at fifth place overall. 

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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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