Making AI actionable within your organization

It can be really frustrating to run a successful pilot or implement an AI system without it getting widespread adoption through your organization. Operationalizing AI is a really common problem. It may seem that everyone else is using AI to make a huge difference in their business while you’re struggling to figure out how to operationalize the results you’ve gotten from trying a few AI systems.

There has been so much advancement in AI, so how can you make this great technology actually translate into actionable business results?

This is a real common problem that has been touching enterprises of all kind, from the biggest companies to mid-sized businesses.

Here are a few quick pointers on how to turn your explorations in AI into AI practices leading to real results from investments.

Pragmatic AI

Firstly, focus on what gets called 'Pragmatic AI', practical AI that has obvious business applications. It’s going to be a long time before we have 'strong AI', so look for solutions that were made by examining problems that businesses deal with every day and then decide to use artificial intelligence to solve the problem. It’s great that your probabilistic Bayesian system is thinking of the world differently or that a company feels like they’ve taken a shortcut around some of the things that make Deep Learning systems slow to train, but what does that mean for the end user of the artificial intelligence? When you’re looking for a practical solution, look for companies who are always trying to improve their user experience and where a PhD in machine learning isn’t needed to write the code.

Internal valuations

Similarly, change the way you are considering bringing an AI solution into your company. AI works best when the company isn’t trying to do a science fair project. It works best when it is trying to solve a real business problem. Before evaluating vendors in any particular AI solution or going out to see how RPA solutions really work, talk to users around your business. Listen to the problems they have and think about what kind of solutions would make a huge difference. By making sure that the first AI solution you bring into your organization aligns to business goals, you are much more likely to succeed in getting widespread adoption and a green light to try additional new technologies when it comes time to review budgets.

And no matter how technology-forward your organization is, AI adoption works best when everyone can understand the results. Pick a KPI focused problem like conversion, customer service, or NPS where the results can be understood without thinking about technology. This helps get others outside of the science project mentality to open their minds on how AI can be used throughout the business.

Finally, don’t forget that AI can help in a wide variety of ways. Automation is a great place to use AI within an organization, but remember that in many use cases, humans and computers do more together than separately and great uses for AI technology help your company’s employees do their job better or focus on the right pieces of data. These solutions often provide as much value as pure automation!

Source: Insidebigdata

SAS: 4 real-world artificial intelligence applications

Everyone is talking about AI (artificial intelligence). Unfortunately, a lot of what you hear about AI in movies and on the TV is sensationalized for entertainment.

Indeed, AI is overhyped. But AI is also real and powerful.

Consider this: engineers worked for years on hand-crafted models for object detection, facial recognition and natural language translation. Despite honing those algorithms by the best of our species, their performance does not come close to what data-driven approaches can accomplish today. When we let algorithms discover patterns from data, they outperform human coded logic for many tasks, that involve sensing the natural world.

The powerful message of AI is not that machines are taking over the world. It is that we can guide machines to generate tremendous value by unlocking the information, patterns and behaviors that are captured in data.

Today I want to share four real-world applications of SAS AI and introduce you to five SAS employees who are working to put this technology into the hands of decision makers, from caseworkers and clinicians to police officers and college administrators.

Augmenting health care with medical image analysis

Fijoy Vadakkumpadan, a Senior Staff Scientist on the SAS Computer Vision team, is no stranger to the importance of medical image analysis. He credits ultrasound technology with helping to ensure a safe delivery of his twin daughters four years ago. Today, he is excited that his work at SAS could make a similar impact on someone else’s life.

Recently, Fijoy’s team has extended the SAS Platform to analyze medical images. The technology uses an artificial neural network to recognize objects on medical images and thus improve healthcare.

Designing AI algorithms you can trust

Xin Hunt, a Senior Machine Learning Developer at SAS, hopes to have a big impact on the future of machine learning. She is focused on interpretability and explainability of machine learning models, saying, 'In order for society to accept it, they have to understand it'.

Interpretability uses a mathematical understanding of the outputs of a machine learning model. You can use interpretability methods to show how the model reacts to changes in the inputs, for example.

Explainability goes further than that. It offers full verbal explanations of how a model functions, what parts of the model logic were derived automatically, what parts were modified in post-processing, how the model meets regulations, and so forth.

Making machine learning accessible to everyone

From exploring and transforming data to selecting features and comparing algorithms, there are multiple steps to building a machine learning model. What if you could apply all those steps with the click of a button?

That’s what the development teams of Susan Haller and Dragos Coles have done. Susan is the Director of Advanced Analytics R&D and Dragos is a Senior Machine Learning Developer at SAS. They are showing a powerful tool that offers an API for a dynamic, automated model building. The model is completely transparent, so you examine and modify it after it is built.

Deploying AI models in the field

You can do everything right when building and refining a machine learning model, but if you do not deploy it where decisions are made it will not do any good.

Seb Charrot, a Senior Manager in the Scottish R&D Team, enjoys deploying analytics to solve real problems for real people. He and his team build SAS Mobile Investigator, an application that allows caseworkers, investigators and officers in the field to receive tasks, be notified of risks and concerns regarding their caseload or coverage area, and raise reports on the go.

Moving AI into the real world

When you move past the science project phase of analytics and build solutions for the real world, you will find that you can enable everyone, not just those with data science degrees, to make decisions based on data. As a result, everyone’s jobs become easier and more productive. Plus, increased access to analytics leads to faster and more reliable decisions. Technology is unstoppable, it is who we are, it is what we do. Not just at SAS, but as a species.

Author: Oliver Schabenberger

Source: SAS

The key challenges in translating high quality data to value

Most organizations consider their data quality to be either 'good' or 'very good', but there’s a disconnect around understanding and trust in the data and how it informs business decisions, according to new research from software company Syncsort.

The company surveyed 175 data management professionals earlier this year, and found that 38% rated their data quality as good while 27% said it was very good.

A majority of the respondents (69%) said their leadership trusts data insights enough to inform business decisions. Yet they also said only 14% of stakeholders had a very good understanding of the data. Of the 27% who reported sub-optimal data quality, 72% said it negatively affected business decisions.

The top three challenges companies face when ensuring high quality data are multiple sources of data (70%), applying data governance processes (50%) and volume of data (48%).

Approximately three quarters (78%) have challenges profiling or applying data quality to large data sets, and 29% said they have a partial understanding of the data that exists across their organization. About half (48%) said they have a good understanding.

Fewer than 50% of the respondents said they take advantage of data profiling tools or data catalogs. Instead, they rely on other methods to gain an understanding of data. More than half use SQL queries and about 40% use business intelligence tools.

Author: Bob Violino

Source: Information-management