Artificial intelligence continues to be the theme of the moment in enterprise software, and today a company out of Asia that has built a suite of AI-powered marketing and ad tools is announcing a round of funding. Appier, a Taipei-based startup that provides an artificial intelligence engine to brands and retailers to help improve customer engagement, predict purchasing and improve conversions on their sites, has picked up $80 million. This Series D includes TGVest Capital, HOPU-Arm Innovation Fund (SoftBank owns Arm), Temasek's Pavilion Capital, Insignia Venture Partners, JAFCO Investment and UMC Capital. The company has raised $162 million to date, with previous investors including Alibaba, Sequoia, SoftBank, and Line. Appier now has around 1,000 customers.

'This is a very timely, comprehensive and well written book in what is now one of the most dynamic and impactful areas of modern applied mathematics. Data science is rapidly taking center stage in our society. The subject cannot be ignored, either by domain scientists or by researchers in applied mathematics who intend to develop algorithms that the community will use. The book by Brunton and Kutz is an excellent text for a beginning graduate student, or even for a more advanced researcher interested in this field. The main theme seems to be applied optimization.

"A year spent in artificial intelligence is enough to make one believe in God" It's futile to deny it, but Artificial Intelligence(AI) is no longer the buzzword of tomorrow, it's a striking reality of today, and the enterprise landscape of AI has never looked more promising than it does today! By 2022, the global business value created by AI will touch a whopping $3.9 trillion, and spending on AI systems is expected to reach $79.2 billion1. Forecasts estimate that AI technologies will pervade every software product2 next year, and AI software revenue is expected to grow to 118.6 billion by 20253. All these are tantamount to the fact that AI is no longer just a differentiator but a core part of business functions! In India, the enterprise AI market is heading towards much wider adoption. An industry expert associates the Indian Enterprise Market for AI to be estimated to be $100 million, growing at 200-250% CAGR.

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Next thing you know, you're leaving the store with a backpack full of groceries and $20 less to your name. And the weirdest thing is that you actually feel good about it. How do supermarkets always get you to buy more than you intended? Grocers have many tricks up their sleeves; from sneaky shelf design that favors premium products to seductive snacks placed by the check-out queues to tempt customers while waiting in the line. But there is something that trumps them all -- sales promotions.

It's no secret that personalized communications, recommendations, and offerings make consumers feel valued and that their favorite brands care about their preferences. Retailers who optimize the consumer experience can anticipate increased satisfaction and loyalty. We've rounded up a few ways to incorporate personalization into all aspects of your consumer's experience to help turn browsers into brand loyalists. Understanding consumers wants and needs can lead to increased consumer satisfaction and loyalty. In fact, 91 percent of consumers are more likely to shop with brands who recognize, remember, and provide relevant offers and recommendations.

Python codes for the above analysis are available on my GitHub, do feel free to refer to them.

Machine learning (ML) lets enterprises unlock the true potential of their data, automate decisions, and transform their business processes to deliver exponential value to their customers. To help you take advantage of ML, Amazon SageMaker provides the ability to build, train, and deploy ML models quickly. Until recently, if you used Amazon SageMaker, you could either choose optimized algorithms offered in Amazon SageMaker or bring your own algorithms and models. AWS Marketplace for Machine Learning increases the selection of ML algorithms and models. In this post, you learn how to deploy and perform inference on the Face Anonymizer model package from the AWS Marketplace for Machine Learning.

The French RoBERTa, aka CamemBERT, is now part of Hugging Face's transformer library. The transformer achieves state-of-the-art (SOTA) results on several NLP downstream tasks: part-of-speech tagging, dependency parsing, named-entity recognition, and natural language inference in French. Need a cheat-sheet for data science or ML? Thanks to this fellow, the biggest payload of cheat sheets in the galaxy covers several programming languages and use-cases is easily accessible on GitHub. One of the biggest retailers on planet Earth is turning to Conversational AI. This past week Walmart announced its partnership with Apple's Siri for peeps looking to buy groceries online -- the service is called Walmart Voice Order.

Artificial intelligence: Safety and Security is a timely and ambitious edited volume. It comprises 28 chapters organized under three distinct themes: security, artificial intelligence and safety. Edited by Roman V. Yampolskiy, the contributions are well integrated and challenge common conceptions. Yampolskiy has assembled a diverse team of leading scholars. In sum, the book provides valuable insight into the cyber ecosystem. It can be read in any order without missing the essence of the subject matter, yet the chapters speak to each other. The chapters provide insight into new research areas and experimental designs. The book is a must-read for computer scientists, security experts, mathematicians, students and individuals who are interested in learning more about the progress of the artificial intelligence field. It will also be of interest to hackers and the intelligence community.