Tom Merritt explains the five ways human error can create issues with machine learning.

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With more than 100 million users registered on its platform, Amazon knows what Indians buy and how one region differs from another. The key driving this know-how is data, which is propelled by the company's machine learning (ML) initiatives. These measures will lay the foundation of artificial intelligence-based shopping in the future.

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We look at the key ways in which machine learning programming dramatically differs from conventional computing.

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62% of highest performing salespeople predict guided selling adoption will accelerate based on its ability rank potential opportunities by value and suggest next steps according to Salesforces’ latest State of Sales research study.

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This past year saw the early stages of AI and ML adoption in the business world begin to solidify. While the growth will continue in 2019, it is still early stage growth. Expect to see more mainstream adoption of many techniques in the next three years.

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It’s exciting to see how B2B companies are taking a page out of Netflix’s book and adopting a similar approach when it comes to the customer experience.

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New methods of solving SI/PI problems using machine learning and artificial intelligence include Bayesian learning, surrogate modeling, and recurrent neural networks.

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AtScale raised $50 million to grow its on-premise and cloud-based data service and to introduce AI-powered features for better decision-making.

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Transportation management systems (TMSs) have a proven ROI. The ARC Advisory Group is excited about the promise of machine learning to allow a TMS to better handle competing objectives and discover nonobvious impacts on performance.

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