21 July 2019
Artificial Intelligence (AI) is fundamentally changing how businesses operate across all sectors, including manufacturing, healthcare, IT, and transportation.
Advancements in AI over the last decade are presenting opportunities for companies to automate business processes, transform customer experiences, and differentiate products offerings.
CBInsights has prepared a list of the major AI trends to follow in 2018.
20 July 2019
What that actually means in practice remains to be seen. It is already clear is threat the following will and is happening:
- End-to-end digitization and optimization of its branch network.
- Enhanced online and mobile offerings
- Re-platforming of mainframes
- Data center consolidation.
- Reduce in-country data warehouses.
- Reduce the number of global instances for personal and business internet banking.
- Use of cloud platforms for non-business critical systems, with HR cited as an example.
- “Streamlining” of critical operations such as know-your-customer, onboarding and payments operations.
- Eliminate 750 legacy systems and applications from the current total of around 6,700.
- The introduction of new core banking system.
- Deliver IT projects more effectively, including more ‘off the shelf’ solutions.
- Increase use of Agile development.
- A goal of fully-automated data transfer between systems.
Digitization Results in Workforce Reduction- Tipping Point Reached
09 July 2019
Training your AI systems efficiently will require a large dataset.
When thinking of powerful AI it is critical to initially concentrate on the data collection required to drive the Machine Learning and Natural Language Processing.
The more data you can collect from the user and send to a well-structured database, the more information your Natural Language Processing and Machine Learning will have to create a world-class AI experience.
Automating the training of machine-learning systems will make AI more accessible.
17 June 2019
Machine learning techniques are now being used to directly detect fast radio bust (FRB) that may be one of the signatures of intelligent life in the universe. Applying machine learning for signal detection promises to open up new avenues for identifying signals from extraterrestrial intelligence.
The SETI Institute has been using IBM Cloud and AI algorithms to analyze over 20 million signals captured by the ATA radio telescopes.
11 June 2019
In early 2014, Srikanth Thirumalai met with Amazon CEO Jeff Bezos. Thirumalai, a computer scientist who’d left IBM in 2005 to head Amazon’s recommendations team, had come to propose a new plan for incorporating the latest advances in artificial intelligence into his division. Amazon’s product recommendations had been built around AI since the company’s very early days,. Amazon also applied AI in its shipping schedules and the robots moving around product in their warehouses.
Prebuilt AI APIs are important tools for cloud platforms to attract and retain customers. Amazon's evolving AI cloud tools will drive the delivery of new use cases for their customers.
11 June 2019
Amazon Machine Learning (AML) offers easy and highly-scalable on-ramp for interpreting data.
AML offers visual aids and easy-to-access analytics to make machine learning accessible to developers without a data science background, using the same technology fuelling Amazon's internal algorithms.
Amazon’s AI philosophy is to make it easier for non-machine-learning experts to apply AI to solve business problems quickly and affordably.