A BUYER’S GUIDE FOR LOCALIZATION SERVICES:

whitePaper | January 12, 2020

“The services market for artificial intelligence technologies is rapidly maturing on the back of several years of learning and a realization—delivering AI is unlike delivering any other technology thus far. Delivering on the promise of AI calls for far more collaboration between service providers, tech vendors, and enterprise clients.” The biggest differentiator in enterprise AI services, as described by most of the customer leaders, is the quality of people—not just the quantity available for a specific skill. Domain understanding and data engineering capabilities are top priorities. A culture of innovation, experimentation, collaboration, and co creation with customers is the key winning formula here.”

whitePaper | July 20, 2022

The growing diversity of actors and circumstances involved in AI/ML deployment makes establishing a set of ethical participatory practices especially difficult for this field. Even among practitioners working at the same organization, there can be substantial differences in both their knowledge about how AI/ML systems were created or will be used and their ability to incorporate inclusive practices. Additionally, the greater availability of AI development platforms, including “no-code” platforms, means that algorithmic models can be deployed without having deep expertise, expanding the number of circumstances automated systems are deployed in. This means many more instances where automated systems are being deployed without consideration of how the algorithms were developed, the provenance of the datasets and nature of the bias on which they were trained and tested, and the ethical implications of their development and deployment.

whitePaper | September 22, 2022

Last year, the UK Department of Digital, Culture, Media and Sport (DCMS) published its 10- year National AI Strategy for the regulation and promotion of artificial intelligence (AI) in the UK (Report). DCMS seeks to build “the most pro-innovation regulatory environment in the world” and vows to make the UK the “best place to live and work with AI” over the next decade. Since then, the UK government and regulators have taken various steps towards this aim, with the Report anticipating a large number of AI-centric publications and consultations. Some key examples (and their latest status) are summarised below, including a policy paper and white paper on a pro-innovation national position on governing and regulating AI, an Information Commissioner’s Office (ICO) consultation on the beta version of its AI and data protection risk toolkit, and a consultation on copyright and patents for AI through the UK Intellectual Property Office (IPO).

whitePaper | December 29, 2022

In our modern world, taking advantage of Artificial Intelligence (AI) to gain insights from data is becoming more prevalent day by day. Graphical Processing Unit (GPU) systems use multiple cores to perform parallel processing, running select workloads to decrease processing times. Compared to GPUs, Central Processing Units (CPUs) have fewer cores; previously, this resulted in less capacity for parallelized processing. To move beyond this limitation, Intel has released new hardware that runs typical AI mathematical computations more efficiently on the CPU, and has also released libraries with hardware optimizations that enable an additional increase in performance.

whitePaper | March 1, 2020

The lucrative business of banks selling their customers “financial advice” coupled with financial products has been in the spotlight recently with the events that have shaken up the banking and financial services world. The most notable ones are the investigations of the Royal Commissions into the misconduct of the banking and superannuation industry1 , the misconduct charges of £264 billion fined by EU on the top 20 banks2 , the Wells Fargo fake accounts scandal3 and the nearly 1000 cases of fraud reported to FINRA resulting in a fine of $61 Million in 20184 .

whitePaper | August 25, 2022

Businesses are in different stages with their artificial intelligence (AI) technologies. Even today, after many years of evaluating AI, only one third of organizations have actually put AI into production (31%1 ). Organizations that started somewhat later are currently prototyping their AI solutions (20%). Companies that only recently came on board with AI are experimenting with AI technologies for their business cases (25%). And a relatively large portion (24%) have only just begun evaluating AI for their business cases.