Amazon's self-driving robots will be roaming the streets of another neighborhood. The online shopping giant said Tuesday that the six-wheeled robots, about the size of a smaller cooler, will begin delivering packages to customers in Irvine, California. It comes after Amazon began testing them in a suburb of Seattle at the beginning of the year. Amazon AMZN, 1.29% said the robots, which are light blue and have the Amazon smile logo stamped on its sides, are able to avoid crashing into trash cans or pedestrians. Still, a worker will accompany the robots at first. Other companies have been testing similar delivery robots on college campuses, delivering burgers or soda to students.

A new program can track all recalled foods based from Amazon customer reviews. Called BERT, the AI program identified thousands of recalled products with an accuracy rate of 74 percent. Researchers from the Boston University School of Medicine developed an artificial intelligence (AI) program that can detect unsafe food contaminated with chemicals, toxins, pathogens, and those which are mislabeled of allergens. Many people experience illness resulting from the consumption of unsafe food items, which is now considered a global health problem. Because of this, the researchers developed a machine learning approach to help detect reports of unsafe food items from Amazon, a multinational technology company and the world's largest online retailer.

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From manufacturing to last-mile delivery, retail giants and startups are using AI and robotics to automate various parts of the chain. Experts believe that players across the retail ecosystem need to adapt to using AI to stay relevant. Corporations across industries are scrambling to adapt and integrate AI into their products, and the retail sector is no exception. In the last five years, retail AI startups that address retailer's AI needs ended up raising around $2.67 billion. For traditional retail giants, leveraging AI means coming close to the competition from e-commerce behemoths like Amazon and Alibaba.

In a bid to cut down on theft and mistakes, Walmart is using artificial intelligence hooked up to cameras monitoring checkouts to ensure every item in your cart is scanned and paid for. This is already happening at over 1,000 Walmart stores. As Business Insider reports, the system is known internally as Missed Scan Detection and uses computer vision to monitor both manned and self-checkout areas at each store. The system is able to detect when an item goes unscanned, or is mis-scanned, and reports the fault to a checkout attendant who can then approach and rectify the problem. According to Walmart spokeswoman LeMia Jenkins, "Walmart is making a true investment to ensure the safety of our customers and associates ... Over the last three years, the company has invested over half a billion dollars in an effort to prevent, reduce and deter crime in our stores and parking lots. We are continuously investing in people, programs and technology to keep our stores and communities safe."

Trevor has been an investor, advisor and operator for cutting-edge technology companies for 15 years with multiple successful exits, and is a regular contributor to business and technology publications such as Inc, Forbes, TechCrunch and Mashable. Trevor currently serves as the CEO of Perch, the leader in interactive Physical Digital retail displays that can detect when customers approach, touch or pick up products and then respond with digital experiences that consistently drive 30-80% sales lift. Trevor is a native New Yorker, an avid fisherman (he caught a 600 lb Black Marlin), an amateur chef and an adventure scuba diver who has dived on every continent including Antarctica.

The grocery story business is beginning to use AI to try to gain a competitive edge. Salt Lake City-based Associated Food Stores (AFS), for example, has 500 stores in the western and southwestern US. It found itself dealing with a growing number of SKUs that stores managers were having difficulty tracking and prioritizing, according to an account in ChainStoreAge. AFS began using an AI solution from CB4 to analyze point of sale data, to identify when physical issues in a store are hold back sales. These could be products not easily visible and out of stock conditions.

Technology is advancing at breakneck speed. It's mind-boggling how they are able to do what we thought only us mortal beings could. Not only do they do these things, they do them better. It is amazing to see how much value they are creating for businesses today. Let's talk about how machine learning, a type of artificial intelligence (AI) has been revolutionizing the ecommerce industry. Simply put, machine learning is a method that uses experience to improve performance over a period of time.

With a few clicks in the Amazon SageMaker console or a few one-line API calls, you can now quickly search, filter, and sort your machine learning (ML) experiments using key model attributes, such as hyperparameter values and accuracy metrics, to help you more quickly identify the best models for your use case and get to production faster. The new Amazon SageMaker model tracking capability is available through both the console and AWS SDKs in all available AWS Regions, at no additional charge. Developing an ML model requires experimenting with different combinations of data, algorithm, and parameters--all the while evaluating the impact of small, incremental changes on performance and accuracy. This iterative fine-tuning exercise often leads to data explosion, with hundreds or sometimes thousands of experiments spread across many versions of a model. Managing these experiments can significantly slow down the discovery of a solution.