Perplexity AI logo is seen in this illustration taken on 4 January 2024. Perplexity AI logo is seen in this illustration taken on 4 January 2024. Amazon sues AI startup over browser's automated shopping and buying feature Amazon sued a prominent artificial intelligence startup on Tuesday over a shopping feature in the company's browser, which can automate placing orders for users. Amazon accused Perplexity AI of covertly accessing customer accounts and disguising AI activity as human browsing. "Perplexity's misconduct must end," Amazon's lawyers wrote.

Text-based dialogues are now widely used to solve real-world problems. In cases where solution strategies are already known, they can sometimes be codified into workflows and used to guide humans or artificial agents through the task of helping clients. We introduce a new problem formulation that we call Workflow Discovery (WD) in which we are interested in the situation where a formal workflow may not yet exist. Still, we wish to discover the set of actions that have been taken to resolve a particular problem. We also examine a sequence-to-sequence (Seq2Seq) approach for this novel task. We present experiments where we extract workflows from dialogues in the Action-Based Conversations Dataset (ABCD). Since the ABCD dialogues follow known workflows to guide agents, we can evaluate our ability to extract such workflows using ground truth sequences of actions. We propose and evaluate an approach that conditions models on the set of possible actions, and we show that using this strategy, we can improve WD performance. Our conditioning approach also improves zero-shot and few-shot WD performance when transferring learned models to unseen domains within and across datasets. Further, on ABCD a modified variant of our Seq2Seq method achieves state-of-the-art performance on related but different problems of Action State Tracking (AST) and Cascading Dialogue Success (CDS) across many evaluation metrics.

AutoML is a powerful capability, provided by Amazon SageMaker Autopilot, that allows non-experts to create machine learning (ML) models to invoke in their applications. The problem that we want to solve arises when, due to governance constraints, Amazon SageMaker resources can't be deployed in the same AWS account where they are used. This post walks through an implementation using the SageMaker Python SDK. It's divided into two sections: The solution described in this post is provided in the Jupyter notebook available in this GitHub repository. For a full explanation of Autopilot, you can refer to the examples available in GitHub, particularly Top Candidates Customer Churn Prediction with Amazon SageMaker Autopilot and Batch Transform (Python SDK).

Financial firms are working to identify potential fraud by analyzing how customers hold their phones, how fast they type and other information about mobile interactions--and the strategy is yielding results. Using artificial-intelligence tools to crunch behavioral data is often a more secure way to verify customers than traditional means such as passcodes, experts say. The Federal Bureau of Investigation warned companies last month that cybercriminals can circumvent typical multifactor-authentication techniques. One way is by calling a telecommunications company, posing as a customer and getting a service agent to switch that person's phone number to the criminal's device. The fraudster can then have the individual's bank send a one-time passcode to the phone and gain access to the target's bank account.

For its inaugural re:Mars conference, Amazon invited attendees it calls "dreamers and builders" -- business leaders, scientists and others who are making contributions to the field of artificial intelligence and machine learning. During the Wednesday keynote at the Las Vegas event, Amazon brandished its own AI credentials, making the case to its potential customers and partners that its teams -- across the business -- are pushing forward the stat of the art. Across every step of its e-commerce operations, AI is at work: Amazon shared the way it uses AI to power e-commerce forecasting, and it showcased StyleSnap, an AI-powered feature that lets shoppers in the Amazon app takes a picture of a piece of clothing and find similar items for sale. Amazon also revealed newest fulfillment center robots, called Pegasus and Xanthus, as well as a new drone that Amazon says will start commercial deliveries within months. For in-store shopping, Amazon revealed new details about the technology that drives its Amazon Go stores.

Fraud detection with machine learning requires large datasets to train a model, weighted variables, and human review only as a last defense. With advances in computer technology and ecommerce also comes increased vulnerability to fraud. Hackers are continuously finding new ways to target undeserving victims, from stolen credit card details to false accounts. Any business or individual who uses online payment sources is open to fraud. In 2015, financial fraud -- including payment cards, remote banking and cheques -- rose a staggering 26 percent from the previous year, totaling a cost of £755 million.

The UK government statistics for 2016 reported that 65% of large firms detected a breach in the previous year, a quarter of which occurred at least once a month. More worryingly, a report by Gartner shows that 80% of all security incidents go undetected by the breached organisations, so the rates of cyber attack are higher than we realise. The costs of cyber attack can be crippling, as highlighted by the media in their coverage of the various incidences that have rocked the IT security world in the past few months. Seemingly robust and industry-leading organisations such as Yahoo have suffered large-scale hacks, while attacks on financial institutions provide very real examples of what customers and businesses stand to lose by being the victim of a cyber attack. When Tesco Bank was hacked in 2016, £2.5 million was stolen from customer accounts, and the recent Lloyds Bank attack saw 20 million customer accounts compromised.