The very mention of Artificial Intelligence reminds most people of movies like the Terminator but in actuality, AI is already very present in our daily lives making things much easier for us in a multitude of fields. For example, according to a Harvard Business Review study, companies that were using AI for sales managed to bring in 50% more leads and reduce their costs by 40%-60%. AI applications are not necessarily actual robots walking about the office. In most cases, it means the introduction of software and tools that make conducting business easier, more affordable, and faster by automating as much as possible. Mathematician Alan Turing was the first to really ask the question'Can machines think?'.

Artificial Intelligence is a very trending topic in the current competitive world. AI is offering smart business solutions to small and large enterprises. The AI is also benefiting the eCommerce in a large way. AI technology has helped e-commerce to reach greater heights. In other words, AI is revolutionizing the mobile app world and the whole structure of businesses.

The dynamic software agents of Robotic Process Automation represent many things to the modern enterprise. They're simultaneously a digital workforce of tireless, ubiquitous employees, a cogent means of actuating statistical Artificial Intelligence, and a critical platform to effect low code workplace automation. Not surprisingly, Gartner projects RPA revenues to increase nearly 20 percent to total approximately 1.9 billion in 2021. But when the agency of these bots is amalgamated across easily accessible interfaces for any number of comprehensive use cases, they become something altogether more than digital agents: they become digital assistants, as easy to use as Siri, yet much more actionable for mission critical workloads. According to Automation Anywhere CTO Prince Kohli, synthesizing the prowess of bots into a digital assistant enables it "to orchestrate a process, which is more than starting and stopping bots.

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.

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?

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."

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.

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.

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.

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.