INDUSTRY ADOPTION (y-axis): Signals include momentum of startups in the space, media attention, customer adoption (partnerships, customer, licensing deals).
MARKET STRENGTH (x-axis): Signals include market sizing forecasts, quality and number of investors and capital, investments in R&D, earnings transcript commentary, competitive intensity, incumbent deal making (M&A, strategic investments).
Trends seeing adoption but where there is uncertainty about market opportunity.
As Transitory trends become more broadly understood, they may reveal additional opportunities and markets.
- Cyber threat hunting
- Conversational AI
- Drug discovery
Trends which are seeing widespread industry and customer implementation / adoption and where market and applications are understood.
For these trends, incumbents should have a clear, articulated strategy and initiatives.
- Open-source frameworks
- Edge AI
- Facial recognition
- Medical imaging & diagnostics
- Predictive maintenance
- E-commerce search
Conceptual or early-stage trends with few functional products and which have not seen widespread adoption.
Experimental trends are already spurring early media interest and proof-of-concepts.
- Capsule Networks
- Next-gen prosthetics
- Clinical trial enrollment
- Generative Adversarial Networks (GANs)
- Federated learning
- Advanced healthcare biometrics
- Auto claims processing
- Checkout-free retail
- Back office automation
- Language translation
- Synthetic training data
Large addressable market forecasts and notable investment activity.
The trend has been embraced by early adopters and may be on the precipice of gaining widespread industry or customer adoption.
- Reinforcement learning
- Network optimization
- Autonomous vehicles
- Crop monitoring
Download the full CB Insights Report Artificial Intelligence Trends in 2019
The goal of this course is to demystify AI
The elements of AI is a free online course for everyone interested in learning what AI is, what is possible (and not possible) with AI, and how it affects our lives – with no complicated math or programming required.
After taking the course, you will be able to:
- Understand some of the major implications of AI
- Think critically about AI news and claims
- Define and discuss what AI is
- Explain the methods that make AI possible
The course is divided into 6 parts that you can go through in order or jump straight to the sections that interest you most.
Chapter 1 -
What is AI?
- I. How should we define AI?
- II. Related fields
- III. Philosophy of AI
Chapter 2 -
AI problem solving
- I. Search and problem solving
- II. Solving problems with AI
- III. Search and games
Chapter 3 -
Real world AI
- I. Odds and probability
- II. The Bayes Rule
- III. Naive Bayes classification
Chapter 4 -
- I. The types of machine learning
- II. The nearest neighbor classifier
- III. Regression
Chapter 5 -
- I. Neural network basics
- II. How neural networks are built
- III. Advanced neural network techniques
Chapter 6 -
- I. About predicting the future
- II. The societal implications of AI
- III. Summary
You are able to take the course at ElementsofAI.com
After taking the course keep updated with Aritificial Intelligence companies in your industry by signing up to Welcome.AI and following categories and companies to receive their latest updates.
The course is self paced but recommended to be completed in 6 weeks and take approximately 5-10 hours to finish each part of the course. Some exercises require a lot of thinking, drawing on paper and going back to the theory part so they can take up to 45 minutes.
The course was designed by Reaktor and the University of Helsinki. The lead instructor of the course is Associate Professor Teemu Roos with industry insights from Hanna Hagström, Director of AI at Reaktor. The course is a part of the AI Education programme of the Finnish Center for AI, and offered in cooperation with The Open University, and Mooc.fi.
The first of its kind available to the global research community, DiF provides a dataset of annotations of 1 million human facial images.
Face recognition is a long-standing challenge in the field of Artificial Intelligence (AI). The goal is to create systems that detect, recognize, verify and understand characteristics of human faces. There are significant technical hurdles in making these systems accurate, particularly in unconstrained settings, due to confounding factors related to pose, resolution, illumination, occlusion and viewpoint. However, with recent advances in neural networks, face recognition has achieved unprecedented accuracy, built largely on data-driven deep learning methods.
To help accelerate the study of diversity and coverage of data for AI facial recognition systems, IBM Research has released a large and diverse dataset called Diversity in Faces (DiF) to advance the study of fairness and accuracy in facial recognition technology.
- 1-million images of human faces from the publicly available YFCC-100M Creative Commons dataset.
- The faces annotated using 10 well-established and independent coding schemes from the scientific literature [1-10]. The coding schemes principally include objective measures of human faces, such as craniofacial features (e.g., head length, nose length, forehead height).
- Studying diversity in faces is complex. The dataset provides a jumping off point for the global research community to further our collective knowledge.
Although science fiction may depict AI robots as the bad guys, some tech giants now employ them for security. Companies like Microsoft and Uber use Knightscope K5 robots to patrol parking lots and large outdoor areas to predict and prevent crime. The robots can read license plates, report suspicious activity and collect data to report to their owners.
These AI-driven robots are just one example of “autonomous things,” one of the Gartner Top 10 strategic technologies for 2019 with the potential to drive significant disruption and deliver opportunity over the next five years.
“The future will be characterized by smart devices delivering increasingly insightful digital services everywhere,” said David Cearley, Gartner Distinguished Vice President Analyst, at Gartner 2018 Symposium/ITxpo in Orlando, Florida. “We call this the intelligent digital mesh.”
- Intelligent: How AI is in virtually every existing technology, and creating entirely new categories.
- Digital: Blending the digital and physical worlds to create an immersive world.
- Mesh: Exploiting connections between expanding sets of people, businesses, devices, content and services.
“Trends under each of these three themes are a key ingredient in driving a continuous innovation process as part of the continuous next strategy,” Cearley said.
The Gartner Top 10 Strategic Technology trends highlight changing or not yet widely recognized trends that will impact and transform industries through 2023.
Read the full article on Gartner.com
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