AI news
An international law enforcement operation led by Britain’s National Crime Agency and the FBI has arrested and indicted members of the Lockbit ransomware gang, in an unprecedented police operation that has struck one of the world’s most notorious cybercrime gangs. The United States has charged two Russian nationals with deploying Lockbit ransomware against companies…
I might have inadvertently insulted Bret Taylor and Clay Bavor when I interviewed them about their new AI startup last week. Their new company, Sierra, is developing AI-powered agents to “elevate the customer experience” for big companies. Among its original customers are WeightWatchers, Sonos, SiriusXM, and OluKai (a “Hawaiian-inspired” clothing company). Sierra’s eventual market…
After months of resisting, Air Canada was forced to give a partial refund to a grieving passenger who was misled by an airline chatbot inaccurately explaining the airline’s bereavement travel policy. On the day Jake Moffatt’s grandmother died, Moffat immediately visited Air Canada’s website to book a flight from Vancouver to Toronto. Unsure of…
In the realm of inferential statistics, you often want to test specific hypotheses about our data. Using the Ames Housing dataset, you’ll delve deep into the concept of hypothesis testing and explore if the presence of an air conditioner affects the sale price of a house. Let’s get started. Testing Assumptions in Real Estate:…
In the vast universe of data, it’s not always about what we can see but rather what we can infer. Confidence intervals, a cornerstone of inferential statistics, empower us to make educated guesses about a larger population based on our sample data. Using the Ames Housing dataset, let’s unravel the concept of confidence intervals…
One of the pre-processing steps that are often carried out on images before feeding them into a machine learning algorithm is to convert them into a feature vector. As we will see in this tutorial, there are several advantages to converting an image into a feature vector that makes the latter more efficient. Among…
Speech recognition technology has become a cornerstone for various applications, enabling machines to understand and process human speech. The field continuously seeks advancements in algorithms and models to improve accuracy and efficiency in recognizing speech across multiple languages and contexts. The main challenge in speech recognition is developing models that accurately transcribe speech from…
The MIT-Pillar AI Collective has announced six fellows for the spring 2024 semester. With support from the program, the graduate students, who are in their final year of a master’s or PhD program, will conduct research in the areas of AI, machine learning, and data science with the aim of commercializing their innovations. Launched…
Self-supervised learning (SSL) has proven to be an indispensable technique in AI, particularly in pretraining representations on vast, unlabeled datasets. This significantly reduces the dependency on labeled data, often a major bottleneck in machine learning. Despite the merits, a major challenge in SSL, particularly in Joint Embedding (JE) architectures, is evaluating the quality of…
Processing extensive sequences of linguistic data has been a significant hurdle, with traditional transformer models often buckling under the weight of computational and memory demands. This limitation is primarily due to the quadratic complexity of the attention mechanisms these models rely on, which scales poorly as sequence length increases. The introduction of State Space…
