In our recent surveys AI Adoption in the Enterprise and Machine Learning Adoption in the Enterprise, we found growing interest in AI technologies among companies across a variety of industries and geographic locations. Our findings align with other surveys and studies—in fact, a recent study by the World Intellectual Patent Office (WIPO) found that the surge in research in AI and machine learning (ML) has been accompanied by an even stronger growth in AI-related patent applications. Patents are one sign that companies are beginning to take these technologies very seriously.
When we asked what held back their adoption of AI technologies, respondents cited a few reasons, including some that pertained to culture, organization, and skills:
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- [23%] Company culture does not yet recognize needs for AI
- [18%] Lack of skilled people / difficulting hiring the required roles
- [17%] Difficulties in identifying appropriate business use cases
Implementing and incorporating AI and machine learning technologies will require retraining across an organization, not just technical teams. Recall that the rise of big data and data science necessitated a certain amount of retraining across an entire organization: technologists and analysts needed to familiarize themselves with new tools and architectures, but business experts and managers also needed to reorient their workflows to adjust to data-driven processes and data-intensive systems. AI and machine learning will require a similar holistic approach to training. Here are a few reasons why:
- As noted from our survey, identifying appropriate business use cases remains an ongoing challenge. Domain experts and business owners need to develop an understanding of these technologies in order to be able to highlight areas where they are likely to make an impact within a company.
- Members of an organization will need to understand—even at a high-level—the current state of AI and ML technologies so they know the strengths and limitations of these new tools. For instance, in the case of robotic process automation (RPA), it’s really the people closest to tasks (“bottoms up”) who can best identify areas where it is most suitable.
- AI and machine learning depend on data (usually labeled training data for machine learning models), and in many instances, a certain amount of domain knowledge will be needed to assemble high-quality data.
- Machine learning and AI involve end-to-end pipelines, so development/testing/integration will often cut across technical roles and technical teams.
- AI and machine learning applications and solutions often interact with (or augment) users and domain experts, so UX/design remains critical.
- Security, privacy, ethics, and other risk and compliance issues will increasingly require that companies set up cross-functional teams when they build AI and machine learning systems and products.
At our upcoming Artificial Intelligence conferences in San Jose and London, we have assembled a roster of two-day training sessions, tutorials, and presentations to help individuals (across job roles and functions) sharpen their skills and understanding of AI and machine learning. We return to San Jose with a two-day Business Summit designed specifically for executives, business leaders, and strategists. This Business Summit includes a popular two-day training—AI for Managers—and tutorials—Bringing AI into the enterprise and Design Thinking for AI—along with 12 executive briefings designed to provide in-depth overviews into important topics in AI. We are also debuting a new half-day tutorial that will be taught by Ira Cohen (Product management in the Machine Learning era), which given the growing importance of AI and ML, is one that every manager should consider attending.
We will also have our usual strong slate of technical training, tutorials, and talks. Here are some two-day training sessions and tutorials that I am excited about:
- Two-day training sessions on TensorFlow, PyTorch, NLP with deep learning, and MLflow
- Given the recent research progress in natural language models, companies are eager to learn how to put these research results to work into their domains. We are happy to announce two new half-day tutorials that will be taught by Lukas Biewald (“Using Keras to classify text with LSTMs and other ML techniques”) and Joel Grus (“Putting cutting-edge modern NLP into practice”).
- Deep learning remains a new topic for many companies, and organizations are interested in augmenting or replacing their existing ML systems with this class of techniques. Neil Conway and Yoav Zimmerman are teaching an important new half-day tutorial—Modern Deep Learning: Tools and Techniques—designed to provide concrete takeaways and best practices for developers, researchers, ML engineers, and technical managers. If your organization is serious about using deep learning, this is a tutorial that you and your colleagues should consider attending.
- Reinforcement learning (RL) remains a popular topic at our AI conference. We have a new tutorial—ML problem-solving with a game engine—that will help participants get started using RL with the Unity engine. A team from RISE Lab will teach an updated tutorial on Ray, an open source distributed computing framework that includes a popular library for RL (RLlib). As I noted in a recent post, Ray continues to grow impressively along multiple fronts, including number of users, contributors, and libraries.
AI and ML are going to impact and permeate most aspects of a company’s operations, products, and services. To succeed in implementing and incorporating AI and machine learning technologies, companies need to take a more holistic approach toward retraining their workforces. This will be an ongoing endeavor as research results continue to be translated into practical systems that companies can use. Individuals will need to continue to learn new skills as technologies continue to evolve and because many areas of AI and ML are increasingly becoming democratized.
Related training and tutorial links:
- Product management in the Machine Learning era
- AI for Managers
- Modern Deep Learning: Tools and Techniques
- Using Keras to classify text with LSTMs and other ML techniques
- Putting cutting-edge modern NLP into practice
- ML problem-solving with a game engine
- Building reinforcement learning models and AI applications with Ray
