Personal Assistant Systems
Can a piece of drywall be smart? Bringing machine learning to everyday objects with TinyML
Since the HAL9000 and Star Trek's M-5 Multitronic, the power and capabilities of AI have always been oversold by both Hollywood and Silicon Valley. Although we're still waiting on machines that can carry on an intelligent conversation, AI has been creeping into many objects in our everyday lives behind the scenes, making them more useful and proactive. People are most familiar with the intelligent assistants built into devices like the Amazon Echo, Google Nest Hub and Apple HomePod, but as I wrote more than three years ago, these rely on cloud backend services for most of their smarts, using local hardware primarily to recognize their wake word and listen for follow-up questions. The combination allows surprisingly sophisticated deep and machine learning models to run on embedded systems. Until recently, shoehorning AI software into a battery-powered device has required data scientists skilled in working with the constraints of an embedded SoC, but recent advances in AI development and automation frameworks, categorically termed TinyML, greatly expands the realm of smart devices.
AI in Marketing: The Power of Personalisation (Part 2).
In my last post, I introduced one of the biggest trends in AI-driven marketing: personalisation. But today's consumers expect personalised messaging with every brand interaction, and luckily, thanks to masses of data, big data techniques, and AI, marketers can deliver it. In this post, I'll show you just how the marketing playbook is being rewritten, using real-world examples from both tech giants and innovative startups*. All of these examples involve machine learning algorithms, which learn to predict a target variable based on patterns in some input data. If you're not so familiar with the workings of machine learning, you can roughly infer how each example works by asking: what hidden variable do I need to solve my problem, and what kind of information would help me predict it?
Artificial intelligence makes 'smart' apps faster, more efficient
A new University of Saskatchewan (USask) artificial intelligence computer model holds promise for making "smart" apps such as Amazon, Apple, and Google's virtual assistants safer, faster and more energy efficient. "Smart" services such as facial recognition, weather forecasting, virtual assistants, and language translators rely on an artificial intelligence (AI) technology called "deep learning" to predict user patterns. But these AI processes often require too much storage to be run locally on mobiles, so the data is sent to external servers over the Internet, which requires lots of power, drains the phone battery, and may increase a user's privacy risk. "My method breaks down the AI computational processes in smaller'chunks' and this helps run the'smart' apps locally on the phone, rather than relying on external servers, while reducing power consumption," said Hao Zhang, a USask electrical and computer engineering post-doctoral fellow. "This research may lead to a different way to design apps and operating systems for our digital devices such as tablets, phones and computers."
Why Amazon Alexa Virtual Assistant Is The Most Intelligent?
DPAs (Digital Personal Assistants) are running our lives after 29 years since the first virtual assistant was launched even if you think you're not using one. The integrating is there for all mobile devices and control how we manage our day to day lives without us evening noticing. Amazon Alexa Virtual Assistant or Alexa launched in 2014 and more than 100m of its Echo and Dot gadgets are available in homes around the world today. In five years, Alexa is topping the DPA market with 8.2 million users around the world and 61.1% of US market shares of smart speakers. Amazon is adding more and more features into Alexa, that is why it's the most intelligent of all assistants in the market.
Serena by Lutron Smart Wood Blinds review: Pretty enough, but also pretty expensive
Lutron makes one of our favorite motorized shades, but the company also offers motorized blinds. Window blinds are considered "hard" window coverings because they consist of slats--wooden, in this case--that drop down from the top of the window (or that slide left or right, in the case of vertical blinds). The motor mounted in the headrail of the Serena blinds tilts the 2-inch slats for privacy and light control. The accumulated weight of the slats, however, makes them too heavy for the motor to lift--even though Lutron fabricates the slats from a soft, fine-grained timber called North American basswood. If you want to fully expose the window, you will need to lift the blinds by hand and pull them back down to close.
Applied AI - Creating machines with human know-how - Ayming UK
Artificial intelligence (AI) is the branch of computer science that enables machines to perform activities that up to now have required human know-how, such as image or speech recognition. Virtual assistants are probably the most familiar everyday manifestations of AI, and the big players in the AI field have their own avatars: Siri (Apple), Alexa (Amazon), Google Assistant, and Cortana (Microsoft). However, the impact of applied AI is now being felt, particularly across sectors such as health sciences. Here, AI-driven solutions automate and improve the efficiency of complex processes for the early detection of certain cancers and reduce the risks to patients in treatment programmes for those conditions. Spending on cognitive and AI systems worldwide is expected to more than quadruple by 2021, according to International Data Corporation.
Heterogeneous Graph Collaborative Filtering
Li, Zekun, Zheng, Yujia, Wu, Shu, Zhang, Xiaoyu, Wang, Liang
Graph-based collaborative filtering (CF) algorithms have gained increasing attention. Existing work in this literature usually models the user-item interactions as a bipartite graph, where users and items are two isolated node sets and edges between them indicate their interactions. Then, the unobserved preference of users can be exploited by modeling high-order connectivity on the bipartite graph. In this work, we propose to model user-item interactions as a heterogeneous graph which consists of not only user-item edges indicating their interaction but also user-user edges indicating their similarity. We develop heterogeneous graph collaborative filtering (HGCF), a GCN-based framework which can explicitly capture both the interaction signal and similarity signal through embedding propagation on the heterogeneous graph. Since the heterogeneous graph is more connected than the bipartite graph, the sparsity issue can be alleviated and the demand for expensive high-order connectivity modeling can be lowered. Extensive experiments conducted on three public benchmarks demonstrate its superiority over the state-of-the-arts. Further analysis verifies the importance of user-user edges in the graph, justifying the rationality and effectiveness of HGCF.
Arlo's new wire-free Pro 4 Spotlight Camera is its best yet
The Arlo Pro 4 is a small but mighty outdoor home security camera. The Arlo Pro 4 Spotlight camera has higher video quality and better field of view than almost any camera we've tested--including the popular Nest Cam Outdoor. Other Arlo Pro 4 features include color night vision output, two-way talk capabilities, timely smart alerts, and easy integration with Amazon Alexa and Google Assistant. The Pro 4 is entirely wire-free and runs on a rechargeable battery that can last up to six months per charge. It also has a built-in spotlight that illuminates when motion is detected, and a smart siren that can be triggered automatically or remotely via the Arlo app.
Apple HomePod mini review: An acceptable Echo alternative
Apple's HomePod was a bit of a tough sell when it launched in 2018. The $350 speaker (now selling for $300) was far more expensive than the Amazon Echo or Google Home, and Siri was less capable and intelligent than Alexa or Google Assistant. One thing was undeniable, though: the HomePod sounded excellent. It was first and foremost for music lovers, and things like smart home controls felt like an afterthought. The HomePod took its place as a niche product while the inexpensive Google Home Mini and Echo Dot gave Amazon and Google a commanding presence in the smart speaker space.
HomePod mini review: Apple's smaller and cheaper smart speaker
Apple's HomePod mini is finally here – the iPhone maker's attempt to break the Amazon Echo-Google Home duopoly and put itself back into the voice assistant race. The HomePod mini costs £99 and sits below the full-sized HomePod costing £279. The speaker looks like a smaller, more spherical version of the big HomePod from almost three years ago. The outside is covered in a recycled plastic fabric mesh and there is a touch-sensitive disc at the top with a coloured LED display that lights up and pulsates as you interact with its voice assistant, Siri. It is an attractive object that is smaller and less prominent than its primary competition, the equally new Amazon Echo and Google Nest Audio.