Science and technology
Science and technology (source: Pixabay)

Judging by the list of countries putting out policy papers on AI and automation technologies, there is very strong interest in AI across the globe. In order to asses the current state of readiness across regions, we recently conducted a survey (full report forthcoming) of the state of adoption of machine learning tools and technologies (a lot of what is being currently described as AI is really ML). The survey yielded 11,400+ respondents, including 2,000 respondents from Europe:

ai adoption

As we assembled the program for our inaugural Artificial Intelligence Conference in London this October, we recognized that many companies and organizations around the world are still very much in the early stages of adoption. Anyone wanting to get started on AI technologies will have to wade through an array of methods and technologies, many of which are still very much on the leading edge. The good news is that some companies at the forefront are beginning to share best practices, tools, and lessons learned as they deploy AI technologies. In this short post, I’ll use key portions of our AI conference in London to describe how companies may want to get started in AI.

AI in the Enterprise: Best practices

ai job titles

Has machine learning impacted how companies organize and build teams? We found that as companies gain more experience with machine learning, they are more likely to start hiring and attracting specialists, including data scientists, research scientists, and machine learning engineers.

But there are many more decisions companies need to make on the path to embracing AI. Organizations must work through a series of assessments in order to successfully deploy AI and automation technologies:

  • What are the key technologies (hardware and software) and what are their limitations?
  • How much data does one need to use these technologies? Where can I get data to augment my existing data sets?
  • How does one organize and hire for AI?
  • What types of problems or tasks lend themselves to automation? What are some initial use cases one can consider?
  • How do you maintain momentum and build upon the lessons learned from previous projects?

We put together training programs, tutorials, and sessions designed to help attendees understand how to move forward with best practices, tools, and technologies used at many leading companies.

Early applications of AI technologies

As I noted earlier, much of the current excitement around AI pertains to recent progress in machine learning—specifically deep learning and reinforcement learning. Both of these class of methods impact existing products and data types (text, temporal, and structured data) and also enable companies to unlock new data sources (including audio, video, and images). Progress in automating enterprise workflows will depend on foundational technologies (hardware and software) and breakthroughs in areas like computer vision, natural language understanding and generation, and speech technologies. We are beginning to see many interesting enterprise applications of both deep learning and reinforcement learning, particularly in computer vision and text, but also in areas where many enterprises already had analytic solutions in place (recommenders and forecasting tools).

Implementing AI

AI applications rely on machine learning models, as well as hardware and software infrastructure for model training, deployment, and management. Machine learning itself requires robust end-to-end data pipelines spanning data ingestion, data preparation, and data management. Depending on the nature of the application, a knowledge base or graph, components for reasoning and planning, simulation platforms, and user interfaces might also come into play. For our upcoming AI conference in London, we assembled sessions on many of core components in an AI technology stack. We have content ranging from tutorials on how to use specific libraries and technologies to how to launch data markets and networks to sessions on best practices for building and architecting AI applications.

Case studies

Among the many the challenges faced by organizations is identifying good use cases for AI and automation technologies. One of our main goals for this conference series is to foster a community of professionals interested in building and using AI applications. To that end, we put together a series of sessions where companies describe how they put AI technologies to work within their organizations. We are also planning a series of attendee networking events at the conference. Here’s a sampling of sessions from a few domains:

Ethics, privacy, security

ai model-building checklist

As I noted in a recent post, there is growing awareness among major stakeholders about the importance of data privacy, ethics, and security. In our recent ML adoption survey, we found that respondents are starting to engage and they are beginning to take into account factors such as bias, transparency, and privacy in their machine learning systems. Fairness and transparency, and privacy-preserving analytics have become areas of active academic research, but it’s important to emphasize that tools and best practices are just beginning to emerge. These concerns cut across geographic regions and industries, but with the launch of GDPR, Europe stands out for taking a leadership position in data privacy. AI policy papers from several European countries also emphasize the need for fairness and transparency in machine learning and AI. With all these considerations in mind, for our inaugural Artificial Intelligence Conference in London we have assembled an outstanding series of presentations on the practical aspects of incorporating ethics, privacy, and security into AI systems.

Article image: Science and technology (source: Pixabay).