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So, the $64 billion question, said Rosina, is: Do we really need full hybrid vehicle today, when we already know it has many drawbacks compared to both mild hybrid electric vehicles (MHEV) and BEV/PHEV?”

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So, the $64 billion question, said Rosina, is: Do we really need full hybrid vehicle today, when we already know it has many drawbacks compared to both mild hybrid electric vehicles (MHEV) and BEV/PHEV?”

SAN FRANCISCO — Semiconductor sales fell further in February, with all major categories of chips experiencing both sequential and year-over-year declines, according to the World Semiconductor Trade Statistics (WSTS), an organization of semiconductor companies that pool sales data.

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The three-month average of semiconductor sales slipped to $32.9 billion in February, down 7.3% compared to January and down 10.6% compared to February 2018, according to the WSTS. February’s decline was steeper than the industry experienced in January, when the three-month average fell by 7.2% sequentially and 5.7% year over year.

Recommended Chip Market Downturn Begins in Earnest

After three consecutive years of record sales between 2016 and 2018 — capped by $468.8 billion last year — the semiconductor industry is widely expected to experience more modest growth or even a sales decline in 2019. The latest WSTS forecast calls for semiconductor sales to increase by 2.6% this year, although most independent analysts are less optimistic.

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In a statement, Semiconductor Industry Association President and CEO John Neuffer noted that February’s sales were down across the board. Sales were also down across all major regional markets, as the global industry continues to endure a period of slowing sales following record revenues over the last three years,” Neuffer said.

February’s chip sales decline was particularly steep in the Americas region, where the three-month average was down by 12.9% sequentially and 22.9% year over year. Sales declined in China by 7.8% sequentially and 8.5% year over year, according to the WSTS.

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Defining what is AI is also problematic: the line between AI on one hand and data science or data mining on the other is blurred, because much of the latter has been repackaged and advertised as AI.

Implementing AI fairness and figuring out how that applies to the product you’re developing is complex: it requires being in the mind of both the practitioner and the user, said Mojsilović. Developers need to understand what fairness means, keep it in mind, and imagine how their product might impact users positively or negatively.

That underlines why diversity is so important. Suppose you’re building a product and its model. You must check the data for balances and imbalances,” she said. Or, say you find out the data was collected inappropriately for the problem you’re trying to solve. You may need to check the model to make sure it’s fair and well balanced. Then, you put the algorithm into production and maybe the model sees users it did not see during its training phase. You have to check fairness then, too. Checking fairness and mitigation has to happen throughout the lifecycle of the model.”

Fairness checking and mitigation tools will be both commercial and open-source, said Mojsilović. Both are equally important, and they will play equally going forward.” Eventually, such tools will be used in more sophisticated systems, so some will be vertical, such as industry-specific or user group-specific. Hybrid open-source/commercial tools systems will also be used.

New technology is often used first where laws and regulations have the most implications to those industries. So it’s not surprising to see many fairness and mitigation tools being developed in finance, where many decisions made by humans and AIs are heavily scrutinized, said Mojsilović. These tools will also benefit the legal professions and credit scoring, other areas where the cost of errors is large.

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