“CognitiveScale finds that, although execs know that data quality and deployment are critical success factors for successful app development to drive digital transformation, more than 76% aren’t sure how to get there in their target 12-18 month window.
‘AI has to be accountable to drive business effectiveness – it’s not sufficient to say a ML model was 98% accurate.’
Instead, the ROI could be, for example, that in order to improve call center effectiveness, AI-driven capabilities ensure that the average call handling time is reduced.
According to Nicola Morini Bianzino, global chief technology officer, EY, thinking of artificial intelligence and the enterprise in terms of “use cases” that are then measured through ROI is the wrong way to go about AI.
‘It’s almost like tending a farm, because the data is living, the data changes and you’re not done,’ he said. ‘It’s not like you build a recommendation algorithm and then people’s behavior of how they buy is frozen in time. People change how they buy. All of a sudden, your competitor has a promotion. They stop buying from you. They go to the competitor. You have to constantly tend to it.’”
“Snap Inc. co-founder and CEO Evan Spiegel on Monday endorsed Elon Musk’s plans to turn Twitter into a ‘super app,’ or an app that provides multiple services in one mobile interface, citing Snapchat’s own ambitions in that arena.
While some tech companies build or acquire separate apps for different services across their portfolio, like Meta or Google, Spiegel said, “’We see the power in diversifying engagement across our service.’
‘When you’ve diversified engagement across a wide variety of products in the same application, that can really strengthen your business,’ he said.”
“Amazon.com today showcased the multiple ways in which artificial intelligence-based machine learning and computer vision algorithms are being combined with synthetic data to improve key retail automation technologies such as Just Walk Out, Amazon One and Amazon Dash Cart.
AI also helps to provide better customer recommendations. For instance, shoppers at Amazon Style, the company’s physical apparel store, will be treated to a diverse list of recommended items based on the products they scan as they peruse the shop floor.
None of this would be possible without the use of synthetic data, though. As Kumar explained, Amazon was challenged by the lack of diverse training data needed to train these algorithms. To compensate, Amazon’s researchers set about building massive sets of synthetic, or machine-generated photorealistic data, that could be used to perfect its algorithms.”
“Ng says that if data is carefully prepared, a company may need far less of it than they think. With the right data, he says companies with just a few dozen examples or few hundred examples can have A.I. systems that work as well as those built by consumer internet giants that have billions of examples.
Ng has some tips that include making sure that data is what he calls “y consistent.” In essence this means there should be some clear boundary between when something receives a particular classification label and when it doesn’t.
The idea of thinking of the building and training of A.I. models as a continuous cycle, not a one-off project, also comes across in a recent report on A.I. adoption from consulting firm Accenture.”