Samsung AI forum 2020 discusses ethics and future of AI
Of course, this year the gathering was virtual. Nevertheless, company executives and leading academics, including representatives from Google, Microsoft and Stanford University, offered their opinions on the next phase of AI development.
One topic of discussion was the best method of approaching human-level intelligence with AI. Dr. Inyup Kang, President of , said of persevering with neural networks versus new algorithms: “We have to try both. Given the scale of human synapses, I doubt that we can achieve the human level of intelligibility using just current technologies. Eventually we will get there, but we definitely need new algorithms, too.”
Professor Yann LeCun of New York University thought that discussion was premature however, believing that a more realistic near future goal would be a machine with a level of intelligence similar to a cat.
Another topic that increasingly captures the public imagination is the ethics of AI, and the presence of biases within the algorithms that dictate our lives. One method of overcoming that is to legislate for diversity at the design stage, as Dr Jennifer Wortman Vaughan of said: “I would like to see regulation around processes for people to follow when designing machine learning systems rather than trying to make everyone meet the same outcomes.”
Also a threat is the presence of manipulated data, with algorithms only being as good as the information that is fed into them. “As AI technology continues to develop, it is important that we stay vigilant about the potential for manipulation and work to solve the issues of any AI systems’ inadvertent data manipulations,” explained Professor Subbarao Kambhampati of Arizona State University.
The advantages and disadvantages of AI in cloud computing
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
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.
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.
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.
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
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.
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.