Collaborating Authors

Representation & Reasoning

The Journey of AI & Machine Learning


Imtiaz Adam, Twitter @Deeplearn007 Updated a few sections in Sep 2020 Artificial Intelligence (AI) is increasingly affecting the world around us. It is increasingly making an impact in retail, financial services, along with other sectors of the economy.

Video streaming device leader Roku debuts new soundbar, player and Roku Channel app

USATODAY - Tech Top Stories

The newest Roku products include a streaming device promising improved video delivery throughout the home, a smaller soundbar that also streams, and an updated mobile app for viewing on the go. The nation's leading streaming platform, Roku said it had about 43 million monthly active accounts at the end of June 2020. Research firm eMarketer estimates Roku captures about 33% of U.S. internet users and 47% of connected TV users. Roku's lineup of devices includes the Roku Express ($29.99) and Roku Streaming Stick ($49.99). But its marquee standalone player – it also markets Roku TVs with built-in streaming capability – is the Roku Ultra ($99.99).

Graph Database: How Graph Is Being Utilised For Data Analytics


In computing, a graph database (GDB) is a database which utilises graph structures for semantic queries with nodes, edges, and properties to represent and store data. The graph related data items in the store to a collection of nodes and edges, where edges are representing the relationships across the nodes. Graph databases are a kind of NoSQL database, built to address the limitations of relational databases. While the graph model clearly lays out the dependencies between nodes of data, the relational model and other NoSQL database models link the data by implicit connections. Graph databases are the fastest-growing category in all of data management.

AI Virtual Assistant using Python


So let's create our own virtual assistant. This is the latest virtual assistant module, created by me. It provides the basic functionality of any virtual assistant. The prerequisite is only Python ( 3.6). The functionality is cleared by the methods name.

An Introduction to AI


I am Imtiaz Adam, and this article is an introduction to AI key terminologies and methodologies on behalf of myself and DLS ( This article has been updated in September 2020 to take into account advances in the field of AI with techniques such as NeuroSymbolic AI, Neuroevolution and Federated Learning. AI deals with the area of developing computing systems which are capable of performing tasks that humans are very good at, for example recognising objects, recognising and making sense of speech, and decision making in a constrained environment. Narrow AI: the field of AI where the machine is designed to perform a single task and the machine gets very good at performing that particular task. However, once the machine is trained, it does not generalise to unseen domains. This is the form of AI that we have today, for example Google Translate.

How can Artificial Intelligence innovate the way we socialise?


Innovation in everything that we do is being driven by technology, including what we do on the internet. From social networking to our online searches, Artificial Intelligence assumes an undeniably significant role in studying our behaviour on digital media platforms and beyond. The greater part of the decisions we make in our day-to-day lives is mostly guided by AI-driven recommendations on our cell phones, personal assistants, chatbots, social network, or other AI technologies. Over 3.8 billion people are actively scrolling through one or the other social media platform such as Snapchat, LinkedIn, or YouTube at any given point of time. All these people and their conversations, searches, likes, dislikes, and more, are being thoroughly read to enable the machine to comprehend their preferences.

At CAGR 36.2%, Artificial Intelligence Market 2020: Future Challenges And Industry Growth Outlook 2025


Artificial Intelligence (AI) is the study of "intelligent agents" which can be define as any device that perceives its environment and takes appropriate action that makes the highest probability of achieving its goals. Additionally, it can also be define as a system's ability to interpret external data, learn from gathered data and use those learnings to realize specific goals through adaptation. It is also called as machine intelligence and attributed to the nature of intelligence demonstrated by machines. Some of the features of artificial intelligence are; successfully understanding human language, contending at the highest level in strategic games systems such as chess and go, autonomously operating cars, intelligent routing in content delivery networks and military simulations and others. To solve the problem of learning and perceiving the immediate environment, many approaches have been taken such as statistical methods, computational intelligence, versions of search and mathematical optimization, artificial neural networks, and methods based on statistic, probability and economics.

Event Stream Processing: How Banks Can Overcome SQL and NoSQL Related Obstacles with Apache Kafka


While getting to grips with open banking regulation, skyrocketing transaction volumes and expanding customer expectations, banks have been rolling out major transformations of data infrastructure and partnering with Silicon Valley's most innovative tech companies to rebuild the banking business around a central nervous system. This can also be labelled as event stream processing (ESP), which connects everything happening within the business - including applications and data systems - in real-time. ESP allows banks to respond to a series of data points – events - that are derived from a system that consistently creates data – the stream – to then leverage this data through aggregation, analytics, transformations, enrichment and ingestion. Further, ESP is instrumental where batch processing falls short and when action needs to be taken in real-time, rather than on static data or data at rest. However, handling a flow of continuously created data requires a special set of technologies.

Scientists use reinforcement learning to train quantum algorithm


Recent advancements in quantum computing have driven the scientific community's quest to solve a certain class of complex problems for which quantum computers would be better suited than traditional supercomputers. To improve the efficiency with which quantum computers can solve these problems, scientists are investigating the use of artificial intelligence approaches. In a new study, scientists at the U.S. Department of Energy's (DOE) Argonne National Laboratory have developed a new algorithm based on reinforcement learning to find the optimal parameters for the Quantum Approximate Optimization Algorithm (QAOA), which allows a quantum computer to solve certain combinatorial problems such as those that arise in materials design, chemistry and wireless communications. "Combinatorial optimization problems are those for which the solution space gets exponentially larger as you expand the number of decision variables," said Argonne computer scientist Prasanna Balaprakash. "In one traditional example, you can find the shortest route for a salesman who needs to visit a few cities once by enumerating all possible routes, but given a couple thousand cities, the number of possible routes far exceeds the number of stars in the universe; even the fastest supercomputers cannot find the shortest route in a reasonable time."