AI is already changing the way financial firms operate, and that change is only going to accelerate. What single change would financial services executives wish for from one wave of a magic wand? Broadridge posed that question to the C-suite executives participating in its latest Digital Transformation and Next-Gen Tech Study. The result was a clear: more artificial intelligence. AI is already changing the way financial services companies operate, and that transformation is poised to accelerate as firms target further expansion and integration of AI as a top strategic priority.

It's been a breakout year for AI in healthcare in particular: The pandemic drove AI-assisted disease research and drug discovery, as companies focused on studying Covid-19 or helping to develop a vaccine. Clinical decision support--the use of AI in diagnosing diseases--was the most active category of AI (in terms of number of deals) in Q3 2020, according to PitchBook data. "We think that the success that AI algorithms are having in diagnosing certain diseases is starting to make the adoption of those technologies feasible, which hasn't been the case historically," says Brendan Burke, a senior emerging technology analyst at PitchBook. Demand will likely rise for "equity engineers," or people who can work with AI tools to help remove biases and make the systems "much more representative of the world around them," says Peter Barrett, founder and general partner at venture fund Playground Global. That's led to an even hotter market for last-mile delivery tech: This year, electric vehicle startups Rivian and Arrival partnered with Amazon and UPS, respectively, on future fleets of electric delivery vans.

Is it finally the year for the rise of the machines? Until not too long ago, AI was just an overused marketing term. Many software vendors who sold solutions based on algorithms and fancy regular expressions branded their stuff as artificial intelligence, even though it wasn't. Times have changed, and the market is--in a helicopter view--divided into two camps: vendors who use a predefined AI framework and vendors who create their own. I'm not looking into the pros and cons of each, but what does this mean for the users?

Speaking to DS News, Gilmore discussed the current state of servicing, COVID-19's impact, and how artificial intelligence and machine learning can be …

Alan Kalton, Vice President and General Manager of Aktana Europe, is a leader in data analytics and manages all new Contextual Intelligence implementations and developments across Europe. He comes to Aktana from Cape Town, South Africa where he led a data analytics venture called BroadReach and prior was the Analytics Leader of EY in South Africa. He also held prominent executive leadership positions in data analytics at IBM, Elsevier, Cognizant, Steris, Novartis, GSK, and ZS Associates. He graduated with a BS and MSc of industrial and operations engineering from the University of Michigan. Kalton can be reached at alan.kalton@aktana.com.

The crystal ball that can predict when, where, and how a customer will spend their money has a new name and it goes by Programmai. This predictive marketing software uses customer data to accurately predict customer spending for both the short and long term. Programmai can even predict the future lifetime value of a customer based on the first interaction. Applying machine learning, marketers can use predictive values from Programmani to develop marketing programs and hone acquisition costs, no longer taking stabs in the dark but instead basing these decisions on justifiable data and forecasts. London TechWatch caught up with CEO Dean Murr to find out how his previous experience at Asos led him to create Programmai, the company's experience raising during the pandemic, and recent funding round.

Data science, Artificial Intelligence (AI) and Machine Learning (ML), since last five to six years these phrases have made their places in Gartner's hype cycle curve. Gradually they have crossed the peak and moving toward the plateau. The curve also has few related terms such as Deep Neural Network, Cognitive AutoML etc. This shows that, there is an emerging technology trend around AI/ML which is going to prevail over the software industry during the coming years. Few of their predecessors such as Business Intelligence, Data Mining and Data Warehousing were there even before these years.

What IT team wouldn't like to have a crystal ball that could predict the IT future, letting them fix application and infrastructure performance problems before they arise? Well, the current shortage of crystal balls makes the union of artificial intelligence (AI), machine learning (ML), and utilisation forecasting the next best thing for anticipating and avoiding issues that threaten the overall health and performance of all IT infrastructure components. The significance of AI has not been lost to organisations in the United Kingdom, with 43 per cent of them believing that AI will play a big role in their operations. Utilisation forecasting is a technique that applies machine learning algorithms to produce daily usage forecasts for all utilisation volumes across CPUs, physical and virtual servers, disks, storage, bandwidth, and other network elements, enabling networking teams to manage resources proactively. This technique helps IT engineers and network admins prevent downtime caused by over-utilisation.

As 2019 ended, many people dug out their crystal balls. Here are five predictions for 2020 that we will check a year from now. They are taken from "Artificial intelligence predictions for 2020: 16 experts have their say" by Ellen Daniel at Verdict UK. One: "Deepfakes will become a serious threat to corporations" – Jesper Frederiksen, VP and GM EMEA, Okta. Deepfakes are highly realistic AI misrepresentations of people and events that could be used to manipulate elections--or perhaps change brand loyalties.

– Blockchain is a mystery story or provides the foundation for cryptocurrencies like Bitcoin. What's different about blockchains compared to traditional big-data distributed databases like MongoDB. Its like featuring a product that contains small blocks of brain in form of dust but consider that the innovation efforts of several publicly traded asset managers and banks are also on this brain block dust quest. Computers start simulating the brain's sensation, action, interaction, perception and cognition abilities. Blockchain is a new approach to manage/monitor financial and other transactions, Guarding an innovation department or powerhouse lab is a smart setup without inbuilt component of artificial intelligence is like an effort of joining blocks without reference of previous block.