Chatbots and other artificial intelligence-assisted customer service tools continue to grow exponentially as retailers look to improve efficiencies. The benefits of chatbots and electronic systems for businesses are relatively straightforward: less time spent monitoring communication channels, less expensive customer service solutions, and even the ability to grow by converting potential customers. Some analysts believe chatbots -- and the evolving capabilities of AI -- could revolutionize industries and propel companies to new levels of success. One analysis found that the global chatbot market is expected to reach more than $10 billion by 2026. So that's the business side; it's pretty clear cut why organizations deploy automated solutions.

Teikametrics, a leading SaaS provider of AI-powered optimization for brands and sellers on Amazon and Walmart, announced the completion of a $15 million strategic funding round backed by new and existing investors. The announcement follows Teikametrics' selection as one of Walmart's first exclusive advertising optimization partners, and the addition of Srinivas Guddanti, a 14-year senior Amazon veteran, as its Chief Product Officer. "We're thrilled to lead this new round of capital in Teikametrics" Jump Capital led the round and were joined by follow-on investments from Granite Point Capital, MIT Professor of Econometrics, Jerry Hausman, and the former Head of Growth at Facebook and Uber, Ed Baker. "We're thrilled to lead this new round of capital in Teikametrics," said Michael McMahon, founding partner of Jump Capital. "The Company has grown rapidly, and the success of its proprietary AI technology for Amazon is a strong proof point for a broader ecommerce platform opportunity. The partnership with Walmart is a landmark event and we are excited to fund the expansion of the Teikametrics platform across multiple ecommerce channels."

USM We strive to deliver exceptional AI services & solutions to help clients meet their unique business objectives. USM We strive to deliver exceptional AI services & solutions to help clients meet their unique business objectives.

The National Health Care Anti-Fraud Association (NHCAA) estimates that healthcare fraud costs the nation approximately $68 billion annually--3% of the nation's $2.26 trillion in healthcare spending. This is a conservative estimate; other estimates range as high as 10% of annual healthcare expenditure, or $230 billion. Healthcare fraud inevitably results in higher premiums and out-of-pocket expenses for consumers, as well as reduced benefits or coverage. Labeling a claim as fraudulent could require a complex and detailed investigation. This post demonstrates how to train an Amazon SageMaker model to flag anomalous post-payment Medicare inpatient claims and target them for further investigation on suspicion of fraud. The solution doesn't need labeled data; it uses unsupervised machine learning (ML) to create a model to flag suspicious claims. This solution uses Amazon SageMaker, which provides developer and data scientists with the ability to build, train, and deploy ML models.

How do brands use Big Data & Machine Learning throughout the customer experience to improve their performance? We outline 3 ways to get started using data. Analysing the data that your business generates is vital to ensuring that you stay ahead in an increasingly competitive landscape. Businesses who adopt data-driven marketing are six times more likely to be profitable year-over-year, and they are more likely to have an advantage over competition (ADS, 2018). Today, data-driven marketing is either embedded or strategic for 78% of marketers and 64% of marketing executives are in strong agreement that data-driven marketing is crucial to success in a hyper-competitive global economy (CMO, 2016).

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Online kernel learning (OKL) is a flexible framework to approach prediction problems, since the large approximation space provided by reproducing kernel Hilbert spaces can contain an accurate function for the problem. Nonetheless, optimizing over this space is computationally expensive. Not only first order methods accumulate $\O(\sqrt{T})$ more loss than the optimal function, but the curse of kernelization results in a $\O(t)$ per step complexity. Second-order methods get closer to the optimum much faster, suffering only $\O(\log(T))$ regret, but second-order updates are even more expensive, with a $\O(t 2)$ per-step cost. Existing approximate OKL methods try to reduce this complexity either by limiting the Support Vectors (SV) introduced in the predictor, or by avoiding the kernelization process altogether using embedding.

Many online platforms act as intermediaries between a seller and a set of buyers. Examples of such settings include online retailers (such as Ebay) selling items on behalf of sellers to buyers, or advertising exchanges (such as AdX) selling pageviews on behalf of publishers to advertisers. In such settings, revenue sharing is a central part of running such a marketplace for the intermediary, and fixed-percentage revenue sharing schemes are often used to split the revenue among the platform and the sellers. In particular, such revenue sharing schemes require the platform to (i) take at most a constant fraction \alpha of the revenue from auctions and (ii) pay the seller at least the seller declared opportunity cost c for each item sold. A straightforward way to satisfy the constraints is to set a reserve price at c / (1 - \alpha) for each item, but it is not the optimal solution on maximizing the profit of the intermediary.

Artificial intelligence (AI), otherwise known as machine learning, is slowly reshaping retail from optimizing back-end supply chain operations to in-store execution. It is also impacting marketing, customer service engagement and anti-fraud activities, according to a report from New York-based information technology industry analyst firm 451 Research. While AI is far from the mainstream, researchers said plenty of retailers are experimenting with how machine learning can be applied in many areas of retail. The report states retailers won't be the only ones needing to adapt to the disruption of machine learning as customers will also face changes in how they view and experience shopping. For AI to work to its full potential, researchers said customers will need to be comfortable with increased data sharing if they want to benefit from personalized shopping experiences via machine learning.

Billie Eilish has fielded countless interview questions during her meteoric rise to fame, but this week was the first time that they've been asked by a bot. Fresh from sweeping the boards at the 2020 Grammy Awards, the 18-year-old songstress sat down with the AI interviewer in a video for Vogue -- and the bot proved a more original interlocutor than many of its human rivals. Its abstract questions provoked some surprising insights into the singer's mind. Viewers learnt that Eilish used to dream of working at Jamba Juice or Trader Joe's, and once wore a wig out to dinner to avoid attracting attention -- but she doesn't want to go back to being anonymous. "How much of the world is out of date?" the AI asked.