Investing in AI can help a business grow, while generating enterprise value. In this article, five experts provide their advice on how businesses can use AI to improve their business and products. "Businesses that deploy AI can expect sales growth through more precisely targeted and relevant customer engagements, more rapid scalability across business operations and greater productivity," says John Michaelis, an expert in the practical aspects of using AI and an experienced business consultant. He is also an active angel investor and board advisor for early-stage AI companies. He provides three essential tips for using AI to grow your business and generate enterprise value.
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.
HSBC is one of the world's largest financial institutions, serving more than 40 million customers globally. One of its largest divisions, Wealth and Personal Banking, supports individuals, families, business owners, investors and entrepreneurs. It provides products and services that include current accounts, credit cards, personal loans and mortgages, as well as savings, investments, insurance and wealth management. At the centre of the Wealth and Personal Banking division is a data analytics group, which is responsible for providing data-tailored services to HSBC teams and customers all around the world. Rahul Boteju, Global Head of Data Analytics at HSBC, was speaking this week at the Big Data LDN event, where he shed some light on what it takes to build an effective data science team that can scale.
Before Covid-19 financial institutions saw a 10:1 ratio of bot-based malicious to legitimate login attempts, according to Aite Group's Fraud & AML practice. Malicious login attempts are setting new records every month. Between 2018 and 2019, there was an 84% increase in the number of breached data reports, reaching 15.1B accounts last year. Fraud operations funded by organized crime run much like legitimate businesses, complete with ongoing recruiting campaigns for AI, bot and machine learning expertise and office locations focused on developing breach strategies. As of June 2020, login credentials for online banking averaged about $35 on the dark web while payment card details averaged between $12 and $20 apiece, according to analysis again by Help Net Security.
See things in your data that no one else can see – and make the right decisions! Due to modern technology and the internet, the amount of available data grows substantially from day to day. And they also know that seeing the patterns in the data gives them an edge on increasingly competitive markets. Proper understanding and training in Machine Learning and Statistical Modeling will give you the power to identify those patterns. This can make you an invaluable asset for your company/institution and can boost your career!
However, one of the ways professionals are keeping up their relevance in their organisations as well as in the industry is by upskilling and learning the latest tools and technologies of this evolving field. Webinars and workshops have always been an excellent way for professionals and enthusiasts to keep themselves updated with the latest trends and technologies. For attendees, these webinars and workshops are not only an easy way to know and train themselves on the latest tools and technologies but also allows them to hear from the best minds of the industry on relevant topics. In fact, for a few years now, large tech companies have been conducting free webinars and workshops, which will not only boosts the community and users at large but also acts as a great marketing tool for advertising their solutions and services. With machine learning being explored in various industries, including healthcare, eCommerce, finance and retail, the possibilities are endless.
The housing market continues to defy gravity. Sales of existing homes rose more than 10% last month compared to a year ago, hitting their highest level since December 2006, according to the National Association of Realtors. And now, more than ever, people are relying on online platforms to search for -- and even buy -- houses. And that opens the door for artificial intelligence to play a bigger role, like using computer vision to create real estate listings based on photos. I spoke with Christopher Geczy, a professor at the Wharton School of the University of Pennsylvania who teaches about real estate and insurance technology.
For most of us, 2020 has ushered in unwelcome chaos and uncertainty. Who would have guessed in early March that within days, personal decisions that were once mundane – like where, when and how to get groceries – would spur paralyzing anxiety. What was missing – and still is – is information and, in particular, data that's helpful. Should I send my kid to school? Is it safe to attend that outdoor wedding?
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.