There is no quick-fix to closing this expectation-reality gap, but the first step is to foster honest dialogue between teams. Then, business leaders can begin to democratize ML across the organization. Democratization means both technical and non-technical teams have access to powerful ML tools and are supported with continuous learning and training. Non-technical teams get user-friendly data visualization tools to improve their business decision-making, while data scientists get access to the robust development platforms and cloud infrastructure they need to efficiently build ML applications. At Capital One, we've used these democratization strategies to scale ML across our entire company of more than 50,000 associates.

We are the leading global information services company, providing data and analytical tools to our clients around the world. We help businesses to manage credit risk, prevent fraud, target marketing offers and automate decision making. We also help people to check their credit report and credit score and protect against identity theft. We employ approximately 17,000 people in 37 countries and our corporate headquarters are in Dublin, Ireland, with operational headquarters in Nottingham, UK; California, US; and São Paulo, Brazil. Experian is committed to creating a diverse environment and is proud to be an equal opportunity employer.

Regulations govern many aspects of citizens' daily lives. Governments and businesses routinely automate these in the form of coded rules (e.g., to check a citizen's eligibility for specific benefits). However, the path to automation is long and challenging. To address this, recent global initiatives for digital government, proposing to simultaneously express policy in natural language for human consumption as well as computationally amenable rules or code, are gathering broad public-sector interest. We introduce the problem of semi-automatically building decision models from eligibility policies for social services, and present an initial emerging approach to shorten the route from policy documents to executable, interpretable and standardised decision models using AI, NLP and Knowledge Graphs. Despite the many open domain challenges, in this position paper we explore the enormous potential of AI to assist government agencies and policy experts in scaling the production of both human-readable and machine executable policy rules, while improving transparency, interpretability, traceability and accountability of the decision making.

Today, Amazon SageMaker Canvas introduces the ability to use the Quick build feature with time series forecasting use cases. This allows you to train models and generate the associated explainability scores in under 20 minutes, at which point you can generate predictions on new, unseen data. Quick build training enables faster experimentation to understand how well the model fits to the data and what columns are driving the prediction, and allows business analysts to run experiments with varied datasets so they can select the best-performing model. Canvas expands access to machine learning (ML) by providing business analysts with a visual point-and-click interface that allows you to generate accurate ML predictions on your own--without requiring any ML experience or having to write a single line of code. In this post, we showcase how to to train a time series forecasting model faster with quick build training in Canvas.

More than a third of companies use artificial intelligence (AI), while another 42% are exploring their AI options, according to IBM's recent Global AI Adoption Index. AI adoption looks easy, thanks to rapid advancements in AI technology and the availability of off-the-shelf AI tools. But in reality, as other recent research makes clear, some companies are struggling with AI. Accenture reports that only 12% of AI adopters are currently using AI "to outpace their competitors," while nearly two-thirds (63%) are still in the experimentation phase--"barely scratching the surface of AI's potential." Multiple reports show that it is common for AI models to never make it into production.

Founded in 2002, Quantium combines the best of human and artificial intelligence to power possibilities for individuals, organisations and society. Our solutions make sense of what has happened and what will, could or should be done to re-shape industries and societies around the needs of the people they serve. As one of the world's fully diversified data science and AI leaders we operate across every sector of the economy and we're growing fast - with growth comes opportunity! We're passionate about building out our team of smart, fun, diverse and motivated people. We combine a team of experts that spans data scientists, actuaries, statisticians, business analysts, strategy consultants, engineers, technologists, programmers, product developers, and futurists – all dedicated to harnessing the power of data to drive transformational outcomes for our clients.

According to Deloitte, compliance costs have risen by 60% for banks and other financial institutions since the 2008 recession. The situation is not much different in other industries as well. As a result, enterprises across the globe are struggling to minimize the cost of compliance under control. Even though there are many ways to keep compliance costs in check, none are as effective as using automation and artificial intelligence. Artificial intelligence and automation can not only increase your efficiency of compliance operations but can also minimize costs.

It's been two years since Mckinsey invented the term analytics translator, called it the'new must-have role' and predicted we'd need around 5 million of them. For the past ten years, we've struggled with the ambiguous title'data scientist', then'citizen data scientist'. Although I've seen many'data scientists' change their Linkedin titles to'analytics translator', the problem remains that no one knows what'analytics translator' really means. Mckinsey seems to have slipped this term into a Harvard Business Review article, and it has somehow taken root. What's more, people seem truly excited by the term.

This year, LinkedIn named data scientist specialists and artificial intelligence practitioners among the top jobs that enterprises sought to fill. The salary range for an artificial intelligence practitioner is $124,000 to $150,000. For a data scientist specialist, the salary range is $100,000 to $130,000. Compare this with the average U.S. salary for programmers, which is under $100,000. If you're working for a company, you want to make as much money as you can -- but you also want to enjoy what you're doing, and you want the self-confidence and assurance that you're providing value in your job and that you yourself are valued.

Automation is one of the most rapidly growing job markets right now, incorporating artificial intelligence (AI), machine learning and robotic process automation (RPA). Businesses are realising the untapped potential of intelligent automation. As more adopt automation, those that do not are becoming less productive and will likely be left behind. An awareness of the value of automation is nothing new, but the boom in demand is largely driven by a need for greater efficiency, rapid deployment and scalability. According to Deloitte's 2020 survey, two-thirds of organisations surveyed also note the Covid-19 pandemic's role in accelerating demand for automation.