How data is represented and operationalized is critical for building computational solutions that are both effective and efficient. A common approach is to represent data objects as binary vectors, denoted \textit{hash codes}, which require little storage and enable efficient similarity search through direct indexing into a hash table or through similarity computations in an appropriate space. Due to the limited expressibility of hash codes, compared to real-valued representations, a core open challenge is how to generate hash codes that well capture semantic content or latent properties using a small number of bits, while ensuring that the hash codes are distributed in a way that does not reduce their search efficiency. State of the art methods use representation learning for generating such hash codes, focusing on neural autoencoder architectures where semantics are encoded into the hash codes by learning to reconstruct the original inputs of the hash codes. This thesis addresses the above challenge and makes a number of contributions to representation learning that (i) improve effectiveness of hash codes through more expressive representations and a more effective similarity measure than the current state of the art, namely the Hamming distance, and (ii) improve efficiency of hash codes by learning representations that are especially suited to the choice of search method. The contributions are empirically validated on several tasks related to similarity search and recommendation.

If you've seen Sci-Fi movies, you very well "know" what AI is! Machines and computers hell-bent on taking over the world and wiping out the human race! Well, they're just fictional movies, so no need to take them that seriously! On a more serious note, AI simply stands for Artificial Intelligence. It's the ability of a computer system, or a machine, to carry out functions that would usually have required human intelligence. To help you better understand the world of AI, we discuss how AI is being used today to help make our day-to-day lives that much easier and simpler.

While the name may sound familiar thanks to Carl Pei's new audio brand Nothing that just launched a pair of true wireless earbuds, Omthing is looking to bring affordable luxury audio products to the country. The company is eyeing a 5% share of India's smart wearables market in 3 yrs and is also looking to set up an R&D centre in India. All three products come with a premium design and an IPX4 rating for dust and water resistance. The Omthing AirFree Lace Neckband is available in black colour option and priced at Rs 1,499 is the cheapest of the three. Omthing AirFree TWS earbuds will retail at Rs 2,499 and are also available in black colour only.

Large-scale conversational assistants like Alexa, Siri, Cortana and Google Assistant process every utterance using multiple models for domain, intent and named entity recognition. Given the decoupled nature of model development and large traffic volumes, it is extremely difficult to identify utterances processed erroneously by such systems. We address this challenge to detect domain classification errors using offline Transformer models. We combine utterance encodings from a RoBERTa model with the Nbest hypothesis produced by the production system. We then fine-tune end-to-end in a multitask setting using a small dataset of humanannotated utterances with domain classification errors. We tested our approach for detecting misclassifications from one domain that accounts for <0.5% of the traffic in a large-scale conversational AI system. Our approach achieves an F1 score of 30% outperforming a bi- LSTM baseline by 16.9% and a standalone RoBERTa model by 4.8%. We improve this further by 2.2% to 32.2% by ensembling multiple models.

The judge said that most of the lawsuit could move forward, despite Apple's request to have it thrown out. Judge Jeffrey S. White, of federal district court in Oakland, did dismiss one piece involving users' economic harm. But he ruled that the plaintiffs, who are trying to make the suit a class action case, could continue pursuing claims that Siri turned on unprompted and recorded conversations that it shouldn't have and passed the data along to third parties, therefore violating user privacy.

Artificial intelligence (AI) is intelligence demonstrated by machines, as opposed to the natural intelligence displayed by humans or animals. Leading AI textbooks define the field as the study of "intelligent agents": any system that perceives its environment and takes actions that maximize its chance of achieving its goals. Some popular accounts use the term "artificial intelligence" to describe machines that mimic "cognitive" functions that humans associate with the human mind, such as "learning" and "problem solving". AI applications include advanced web search engines, recommendation systems (used by YouTube, Amazon and Netflix), understanding human speech (such as Siri or Alexa), self-driving cars (e.g. Tesla), and competing at the highest level in strategic game systems (such as chess and Go), As machines become increasingly capable, tasks considered to require "intelligence" are often removed from the definition of AI, a phenomenon known as the AI effect.

This is a guest authored post by Yu Chen, Senior Software Engineer, Headspace. Headspace's core products are iOS, Android and web-based apps that focus on improving the health and happiness of its users through mindfulness, meditation, sleep, exercise and focus content. Machine learning (ML) models are core to our user experiences by offering recommendations that engage users with new relevant, personalized content that builds consistent habits in their lifelong journey. Data fed to ML models is often most valuable when it can be immediately leveraged to make decisions in the moment, but, traditionally, consumer data is ingested, transformed, persisted and sits dormant for lengthy periods of time before machine learning and data analytics teams leverage it. Finding a way to leverage user data to generate real-time insights and decisions means that consumer-facing products like the Headspace app can dramatically shorten the end-to-end user feedback loop: actions that users perform just moments prior can be incorporated into the product to generate more relevant, personalized and context-specific content recommendation for the user.

Colby College has hired a language processing expert to lead its newly formed Davis Institute for Artificial Intelligence. Amanda Stent, considered one of the country's leading authorities on natural language processing – which gives computers the ability to understand human text and spoken words – will start in October. Stent most recently served as the natural language processing architect at Bloomberg L.P., where she led the People and Language AI Team. Stent has authored or co-authored more than 100 papers on natural language processing and is a regular speaker on the subject. She was also involved in the CALO (Cognitive Assistant that Learns and Organizes) project that led to a range of AI applications, including the well-known virtual assistant Siri.

One of Samsung's latest ultra-short throw projectors has been discounted by $1,000 as part of a larger sale on Amazon. The Premiere Projector with a 4.2.2 channel sound system is down to $5,498 -- yes that's still quite expensive even for a projector, but it's a much better buy now than at it's normal $6,500 price. We've only seen it cheaper in June during Prime Day when it was an additional $300 off. If you feel comfortable sacrificing on sound, the same model with a 2.2 channel system has dropped to $2,998. Samsung's matching both prices, so you could buy direct from the company if you prefer.

Microsoft has announced that Windows 11 will be available on October 5th as a free upgrade for qualifying Windows 10 systems, as well as on new PCs shipping after that date. But it isn't for everyone; a gradual rollout will prioritize newer hardware and use "intelligence models" to determine who gets the upgrade first. Microsoft will apparently factor in reliability and device age. It could be the case that friends and family utterly disinterested in an OS update could be offered it ahead of anyone champing at the bit for the latest edition of Windows. All supporting machines will get the update by mid-2022, if you can think that far ahead.