CIOs shouldn't wait for an ethical AI framework to be mandatory. Whether buying the technology or building it, they need processes in place to embed ethics into their AI systems
Only 20% of companies report having an ethical artificial intelligence framework in place and just 35% have plans to improve governance of AI systems and processes in 2021, according to PwC data. It's a problem. No wonder Biden's working on an AI bill of rights.
I believe every CIO needs a responsible AI plan before implementing the technology. Businesses shouldn't wait for this to be mandatory. It doesn't matter if the CIO is buying the technology or building it. AI as a technology is neutral -- it is not inherently ethical or unethical. We need processes in place to confirm that ethics is embedded in AI systems.
Leaders must assess the business value, sophistication and track record of AI products before committing to an AI solution
Consumers and employees are growing accustomed to the benefits of AI-supported products, putting pressure on executives with purchasing power.
Employees want to find AI products in their work toolkit that can make workflows easier and amplify their efficiency. Consumers want the companies they patronize to be speedy and accurate in everything from customer service to e-commerce suggestion engines.
Business is bullish on AI, but it takes a well-developed understanding to deliver visible business benefits
Artificial intelligence technologies have reached impressive levels of adoption, and are seen as a competitive differentiator. But there comes a point when technology becomes so ubiquitous that it is no longer a competitive differentiator -- think of the cloud. Going forward, those organizations succeeding with AI, then, will be those that apply human innovation and business sense to their AI foundations.
Such is the challenge identified in a study released by RELX, which finds the use of AI technologies, at least in the United States, has reached 81% of enterprises, up 33 percentage points from 48% since a previous RELX survey in 2018.
One challenge in scaling edge is that it's nearly everywhere, compared to the more centralized cloud, according to Mishali
Another challenge is training edge AI models.
"The approach today for edge AI is to do the inference very close to the data source," Mishali said.
He added that for training, data needs to be captured and then sent to a central data center or to the cloud. This requires a lot of power, and a lot of GPUs, to do effective training.
"This can be a real challenge for scaling applications," Mishali continued, adding that enterprises face the difficult challenge of making sure that data flows smoothly among numerous endpoints and bandwidths.
Enterprises across every industry are increasingly recognizing the power of machine learning. It can turn any organization's data into valuable insights-insights that have the potential to revolutionize every aspect of the business.
Machine learning (ML) is a type of data analysis based on the concept that systems can use data to learn with little to no human effort needed. ML systems process data to identify patterns, ferret out anomalies, and recognize subtle correlations that people wouldn't notice. It gives organizations deeper insight into how and why some workflows are profitable or efficient and others aren't. It can help prevent fraud, eliminate production bottlenecks, inform sales and marketing professionals of what tactics work with which target audiences, and so much more.
Artificial intelligence (AI) has turned into a highly pervasive technology, and it has been incorporated in a wide array of industries across the globe
"The tough competition in the market and success stories surrounding AI adoption are among the few major factors that compel more and more enterprises to adopt AI in various aspects of their business.
Machine learning (ML), the key component of AI technology, has become powerful to the level of displaying superhuman capabilities on most human tasks. However, this superhuman performance comes with higher complexity in the AI and ML models, turning them into a 'black box,' a decision-making model too complex to be understood by humans..."
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