TechRadar : Artificial Intelligence Technologies, Q1 2017

Transcription

1 TechRadar : Artificial Intelligence Technologies, Q And Unburden Your Employees by Rowan Curran and Brandon Purcell Why Read This Report Artificial intelligence (AI) is an old idea. But a broad set of important technologies is emerging, which big data, cloud storage, and compute have enabled. AI is a liberatory technology at its core, and businesses that integrate it will free workers to become more innovative, creative, and adaptive than ever before. But these technologies are still in early stages. In this report, we examine 13 of the more important technologies for enterprises and application development and delivery (AD&D) pros to consider integrating into apps and business processes to support human decision-making. Key Takeaways AI Technologies Will Augment And Enhance Human Work AI technologies can fully replace humans in some scenarios. But most cases of AI will be where machine intelligences augment human work because they react faster and process more inputs than a human does. AI Systems Still Demand Considered Design, Knowledge Engineering, And Model Building The goal of many AI systems is to have a functionally autonomous application. But an AI system requires significant human effort to design, engineer the knowledge that it represents, and build the models for taking inputs and executing actions. AI Technologies Demand New Skills, Not A New Team AI technologies often require new skills to use such as familiarity with deep learning, text analytics, and emotional computing. However, building an entirely separate AI team is not the answer. You can build these intelligent systems with existing development and data science teams, albeit with deeper partnerships among them and novel new roles. FORRESTER.COM

2 And Unburden Your Employees by Rowan Curran and Brandon Purcell with Srividya Sridharan, Holger Kisker, Ph.D., Martha Bennett, Boris Evelson, Michele Goetz, Mike Gualtieri, Rob Koplowitz, Craig Le Clair, Diego Lo Giudice, Jennifer Wise, and Emily Miller Table Of Contents AI Technology Helps Business Liberate And Scale Human Labor Overview: TechRadar For Artificial Intelligence Technologies AI Technologies TechRadar: Time To Put On Your Training Wheels Recommendations Adopt Maturing AI Technologies, And Pilot Ones With Potential What It Means AI Will Transform Business And Operate As An Ecosystem Supplemental Material Notes & Resources Forrester interviewed 64 vendor and 12 user companies, including [24]7, Adgorithms, Affectiva, Clarabridge, Clarifai, Coveo, Creative Virtual, Earley Information Science, Google, Hewlett Packard Enterprise (HPE), IBM, IPsoft, Lexalytics, Living Actor, Loop AI Labs, Luminoso Technologies, Maana, Narrative Science, Salesforce, Software AG, and Verint Systems. Related Research Documents Artificial Intelligence: What s Possible For Enterprises In 2017 The Future Of Jobs, 2025: Working Side By Side With Robots TechRadar : Artificial Intelligence Technologies And Solutions, Q Forrester Research, Inc., 60 Acorn Park Drive, Cambridge, MA USA Fax: forrester.com 2017 Forrester Research, Inc. Opinions reflect judgment at the time and are subject to change. Forrester, Technographics, Forrester Wave, RoleView, TechRadar, and Total Economic Impact are trademarks of Forrester Research, Inc. All other trademarks are the property of their respective companies. Unauthorized copying or distributing is a violation of copyright law.

3 AI Technology Helps Business Liberate And Scale Human Labor AI is nothing new. 1 From 2001: A Space Odyssey s Hal 9000 to Star Wars C-3PO and the agents in The Matrix, we ve all come across fictional representations of AI throughout our lives. 2 In the real world, AI research has been progressing since the 1950s and got lots of attention in the 1980s around expert systems. But these systems failed to live up to expectations. 3 Today, we ve entered a new renaissance due to increasingly cheap and powerful compute, storage, and networking. AI capabilities that were in the realm of science fiction in the not-too-distant past are within reach of a broad range of enterprises, government, and private citizens. 4 AI can help raise top-line profits and win new customers as well as mitigate risks and increase the efficiency of the bottom line. 5 In these early days of AI s resurgence, AD&D pros can select AI technologies by considering not only how they can use them to replace human workers but also how these technologies can: Amplify human intelligence. Today, AI mainly acts as an augmenting intelligence it enhances the intelligence of humans by providing them with contextual knowledge from data that the human mind alone can t access and process. Companies are using combinations of technologies that we covered in this report to better understand customer context and activities. These support combinatorial use cases like predictive lead scoring, combined with a training chatbot, to make salespeople smarter; automated process guidance and knowledge search for customer support associates; and analysis of best practices and pitfalls of development decisions for software developers. 6 Liberate employees from banal or onerous tasks. Many responsibilities at enterprises require little cognitive effort on the part of humans, but they have been historically out of reach of machine intelligence. That list of responsibilities is rapidly shrinking. AI technologies allow companies to understand large volumes of content using image and video analysis (and transform that complicated unstructured data into more-comprehensible structured data sets). They also handle low-level customer service requests using speech analytics, chatbots, as well as text analytics and natural language processing (NLP) or even automate customer sign-ups and onboarding using advanced business process automation, combined with naturalistic interfaces. 7 Enable robotic processes for self-healing and self-correcting systems. AI technologies can also be extremely valuable in scenarios where there will never be a direct interaction with a human being beyond the setup and deployment. For example, autonomous software agents today manage complex technology management infrastructures and help mitigate and recover from disasters with little to no human intervention. In some of these cases, machines can even come up with novel, superior solutions that emerge from the combined information in their knowledge representation systems. 8 2

4 Investment In AI Technologies Rises, But Challenges Remain In May and June 2016, we ran a custom panel survey asking enterprises about their investment in AI technologies. What we found was strong interest in investing in AI technologies across many different industries, and we found that many companies have already implemented components of an AI ecosystem successfully. 9 But AI is not a homogenous set of technologies, and some tasks will take longer to automate than others. 10 Despite the fact that the goal of AI technology is to free humans from performing certain tasks so that they can more effectively focus on higher-level and higher-value thinking, the process of creating this state has significant challenges for the human designers and engineers. In our research, we found that some of the key challenges obstructing the adoption of AI technology include the lack of: A clear business case. Since the current AI renaissance is a relatively recent phenomenon, many organizations don t understand how to apply AI to meet specific business objectives. As with many technologies, researchers and academia were the first to develop and deploy AI, and businesses are just beginning to jump on the bandwagon. Without a well-worn path to ROI, however, many organizations have difficulty justifying investment at this point (see Figure 1). Specialized skills to build, deploy, and manage AI systems. If data scientists are unicorns, then specialists in AI are their even more rarely mentioned winged cousin Pegasus. 11 There are a handful of notable researchers in academia who specialize in deep learning and AI, but the talent pool for businesses is extremely shallow. Additionally, since AI adoption for business is so nascent, even fewer people have the ability to deploy AI in a business context. This doesn t necessarily mean that you ll need to completely rebuild teams tomorrow, but you will need new members. For example, a major international consulting company is adding a linguist to its team to build an application that automatically classifies textual content. A robust data management platform. Garbage in, garbage out is especially applicable to artificially intelligent systems. These systems often need massive amounts of training data to learn to perform a specified task. Since 43% of analytics professionals cite ensuring data quality from a variety of sources as their biggest analytics challenge, many organizations are clearly not ready for AI yet. 12 As Microsoft painfully learned from its attempt to launch a chatbot on Twitter, AI is only as good (or bad) as the data that feeds it and the management and governance of that data s influence on training. 13 Change management processes and practices. Beyond the challenges around business cases, skills, and data, one of the biggest organizational impacts of AI systems, and one that businesses are most concerned about, is the impact of such systems on the enterprise itself. 14 Nearly onethird of our survey respondents found that change management was one of their biggest risks and challenges, with 16% already restructuring their workforce due to AI and 13% more in the planning stages. Only one in five respondents did not expect that AI would cause them to restructure in the next three years. 3

5 FIGURE 1 AD&D Pros Can t Justify Investment In AI Technologies Without A Defined Business Case You indicated that your organization is not ready or has no plans to invest in AI. What do you feel is needed before investing? (Please select up to three) There is no defined business case for AI investment We are unclear what AI can be used for in our business We do not have the skills to build and support an AI system We need to invest in modernizing our data management platform first We do not have the budget We are unsure what is needed to build or roll out an AI system We do not feel AI systems are proven We do not have the right processes and governance It is a lot of hype with little substance or new ideas We do not have or have access to the data we would need Other I am confused by AI; not sure what it means 42% 39% 33% 29% 23% 19% 14% 13% 11% 8% 6% 3% Base: 172 business and technology professionals at firms with no plans or are not ready to invest in AI technologies Note: Don t know responses were excluded from analysis. Source: Forrester s Q Global State Of Artificial Intelligence Online Survey Overview: TechRadar For Artificial Intelligence Technologies To help AD&D pros plan their next decade of investments in AI, Forrester investigated the current state of 13 important technology categories. We examined past research, interviewed 12 experts in the field, and experimented with very early versions of products and services. We also conducted detailed research with multiple current or potential customers and users of each of the TechRadar categories, for a total of 12 customers and users. We used the data thus collected to assess four factors for each of the included TechRadar categories: 1) the current business value; 2) the potential to add more business value in the future (i.e., the overall trajectory, from minimal success to significant success); 3) the current market maturity (i.e., ecosystem ); and 4) the time to reach the next stage of maturity. 15 4

6 Why Do These 13 Categories Appear In The TechRadar? AI is a very broad discipline and given the number of applications and use cases both general-purpose and domain-specific, we feature the 13 technologies that we believe are likely to have the greatest influence on the development of the AI market during the coming years. Specifically, each of these TechRadar categories: Is or will play a prominent part to increase human-machine collaboration. The technologies in the TechRadar must, at their core, support automation of some piece of the business and amplify human expertise, augment knowledge, or unburden humans of repetitive tasks. Is available commercially from at least one vendor and not just a research project. Beyond the technologies in the TechRadar, there is ongoing research and development around many other technologies. These are not included in the TechRadar due to their nascent stage and the lack of diversity in technologies that are currently available. Has potential business value in many different domains. The technologies in this TechRadar support use cases across industries and across a single enterprise. These are the core technologies that enterprises need to build AI, and we focused this research on them rather than solutions or applications for very specific use cases. 16 AI Technologies TechRadar: Time To Put On Your Training Wheels In our research of AI technologies, we found that one of the loudest tropes in the general and technology press was a fear of replacement of humans by machines. But the reality is quite different, and we predict a hybrid future of humans and machines working together to increase overall efficiency. The investment in building and applying these technologies is quickly increasing, and application developers should consider experimenting with AI if they are not already (see Figure 2, see Figure 3, and see Figure 4). When considering prototype use cases, developers should keep some results from our research in mind that will be both surprising and reassuring: Humans and machine intelligences work best in tandem. Much of the present worry about AI systems stems from an anxiety about technological unemployment. 17 However, most AI systems aren t always and only replacing human employees; they are often deflecting low-value tasks, which are too expensive or onerous to have humans complete. This dynamic applies to AI systems too. They cannot build their intelligence without the input and support of the humans who construct models, engineer knowledge representations, impart tribal expertise (not captured in digital systems), and monitor and maintain these systems once they are up and running. In essence, AI technologies mimic humans abilities to sense, think, and act while constantly learning. But humans need to connect the technologies from all three sensory components to build AI systems that will sense, think, and act on their own. 5

7 Human-to-computer interaction far outpaces computer-to-human interaction. We are just beginning to scratch the surface of how machines can most efficiently communicate back to us, and this is an area that has received little attention because most vendors focus on text and visualizations through standard charting and graphs. While virtual agents that interact through chat and messaging platforms are getting a lot of attention, we noticed a striking asymmetry between the two halves of these conversations. The sophistication of NLP and human recognition technologies in understanding and interpreting humans intents and queries are far ahead of the necessary natural language generation (NLG) to deliver insights and actions back to humans in a more naturalistic way. Experiments in nontraditional representations of information and knowledge, such as the visual representation of Watson on Jeopardy, is an early foray in this space. 18 Narrower use cases yield better results. Pure AI does not exist today. Companies that have seen real business success in implementation of these systems have kept the scope of the use cases narrow and developed their applications so that the AI does not need to understand intent (a challenging question) and merely responds to queries, questions, or inputs within a limited scope. Successful implementations include virtual agents for customer service, the use of image analysis for security and surveillance, and interactive voice response (IVR) systems. 19 6

8 FIGURE 2 AI Technologies Mimic Humans Abilities To Sense, Think, And Act Sense Sense Image and video analysis Biometrics Speech recognition Text analytics and NLP Think Learn Machine learning platforms Deep learning platforms Semantic technology AI-optimized hardware Swarm intelligence Act Think Act Natural language generation Decision management Sense, think, and act Robotic process automation Virtual agents 7

9 FIGURE 3 Connecting The Components To Build AI Systems Sense Customer, partner, employee, robot, and device HUMAN RECOGNITION Speech, face, and body MACHINE RECOGNITION Sensors (e.g., temperature, chemical, spectral, magnetic) and devices Act INTERFACE Virtual agents and natural language generation AUTOMATION Robotic process automation and decision management AI-OPTIMIZED CHIPS Learn Continuous iteration and feedback KNOW Knowledge representation, rules engines, corporate data, open data, and external data Think LEARN Machine learning platforms, deep learning platforms, text analytics and NLP, and image and video analysis 8

10 FIGURE 4 TechRadar : Artificial Intelligence Technologies, Q1 17 Trajectory: Significant success Moderate success Minimal success Time to reach next : <1 year 1 to 3 years 3 to 5 years 5 to 10 years >10 years Decision management Business value-add, adjusted for uncertainty High Medium Low Negative Swarm intelligence Natural language generation Speech recognition Deep learning platforms Biometrics Semantic technology AI-optimized hardware Machine learning platforms Virtual agents Text analytics and NLP Robotic process automation Image and video analysis Creation Survival Growth Equilibrium Decline Ecosystem Creation: More Technologies Will Emerge With Increased AI Innovation The Creation is for technologies that are available on a limited basis with a few specialized vendors in the market; technology is limited to implementation for specialized use cases. The potential business value of these solutions is uncertain, but it may be much higher than that of other categories in certain industries or for certain use cases. Because AI is an old and already well-researched field, many technologies have moved out of the Creation into the Survival. However, many new technologies will enter this in the future. We ve placed three technologies in the Creation today: 9

11 Deep learning platforms. Deep learning focuses on the creation of digital neural networks that mimic the interaction of neurons in the human brain (see Figure 5). Today, these algorithms are used to detect objects in images, analyze sound waves to convert spoken speech to text, or process natural human language into a structured format for analysis. This technology is still in its early days for business use but is beginning to expand to more diverse areas such as fraud detection or improved customer experiences by boosting digital content s effectiveness. 20 Developers interest and adoption have also grown significantly and support a growing community of open source options such as H2O.ai, TensorFlow, Caffe, Theano, and Deeplearning4j (DL4J). Natural language generation. This set of technologies enables machines to more smoothly interact with humans by delivering information, insights, and interactions through natural-language sentences and longer texts (see Figure 6). It involves a combination of rules that humans or statistical analysis generate, algorithmic modeling of knowledge and semantics, and supporting libraries of answers and knowledge. This technology is currently providing value in areas such as media content production for online newspapers or financial reports at scale. Building applications with NLG still requires a significant amount of setup and engineering of questions and responses; it can t yet consistently generate natural language from scratch. However, advances in generative algorithms and an ever-growing set of well-labeled training data are bringing this closer to a reality. Swarm intelligence. Swarm intelligence is the concept of using a large group of autonomous agents, either humans or software, which each contribute a piece of a solution to a problem the whole is literally more than the sum of its parts (see Figure 7). This produces an emergent intelligence by leveraging each individual s specific knowledge to contribute to a consensus or best-in-class answer. This area is still in the early stages but has seen success in some use cases, such as using algorithms that mimic the behavior of social insects to accomplish business objectives like more efficient routing of trucks for deliveries and supply chains. Other companies are taking a different approach by attempting to harness swarms of human intelligence to produce collective answers, such as picking stocks, predicting sports outcomes, or answering questions. 10

12 FIGURE 5 TechRadar : Creation Phase, Deep Learning Platforms, Q1 17 Element Definition Usage scenarios Vendors Estimated cost to implement Categorization (if applicable) Explanation Deep learning (DL) platforms provide access to DL algorithms, a specific type of machine learning approach consisting of neural networks with multiple abstraction layers. DL has emerged as the predominant method in AI today, due to its ability to extract features from unstructured data such as images in order to classify them. Platforms in this category provide algorithms, APIs, development and training toolkits, data, and computing power to design, train, and deploy the DL algorithms and models. Today, DL platforms are primarily used to solve problems of pattern recognition and automation across very large and complex data sets. Because these techniques require well-labeled known data sets to build the initial models, usage scenarios are largely limited to image and video recognition (e.g., identifying people, objects, or places) and auditory analysis (e.g., identifying human speech or the direction from which a sound was generated). An emerging usage scenario is in fraud detection and risk analysis, where DL models are proving more flexible in detecting the changing tactics of fraudsters than their machine learning cousins. Amazon (DSSTNE), Deep Instinct, Ersatz Labs, Fluid AI, H2O.ai, MathWorks (MatLab Deep Learning ToolBox), Peltarion, Saffron Technology (The Natural Intelligence Platform), SAS, Sentient Technologies, and TensorFlow DL platforms can be expensive to purchase and implement, especially because many companies are seeking to deploy them on graphics processing units rather than CPUs. The cost to adopt and implement DL platforms is typically in the range of hundreds of thousands of dollars, with large-scale deployment ranging into the millions or tens of millions. Ecosystem Business value-add, adjusted for uncertainty Time to reach next Trajectory (known or prospective) Creation Negative 3 to 5 years Moderate success The application of DL to business is still relatively nascent. Many of the platforms have been developed by academics; these platforms still take considerable time and effort to train with data. Due to the time and effort it takes to train DL models, these platforms are creating negative value today. The use of these methods is expected to grow significantly over the next three to five years as enterprises produce more data sets appropriate for modeling with DL. Increasing availability of broader data sets as well as the skills and imagination to train and apply them will drive significant growth in this area. Deep learning will benefit from a network effect as the community grows leading to better DL platforms, both proprietary and open source. Wider adoption at the enterprise level will also likely occur as more methods are developed to examine DL models that understand the black box. 11

13 FIGURE 6 TechRadar : Creation Phase, Natural Language Generation, Q1 17 Element Definition Usage scenarios Vendors Estimated cost to implement Categorization (if applicable) Explanation Natural language generation (NLG) comprises tools and technology that use advanced modeling to produce human-readable text, usually from a corpus of responses or composed from defined textual components. NLG is used to scale human content generation capabilities by automating some financial and news reports, augmenting visualizations, delivering insights in business intelligence and advanced analytics tools, and enabling virtual agents to interact more naturally with humans. Attivio, Artificial Solutions, Basis Technology, Cambridge Semantics, Captricity, Coveo, Digital Reasoning, EPAM, Expert System, Google (Natural Language API), Hewlett Packard Enterprise, IBM, indico, kcura (Content Analyst), Knime, Lexalytics, Lexmark International, Linguamatics, Lucidworks, Luminoso Technologies, Mindbreeze, OpenText, SAS, Salesforce, Sinequa, Skymind, and Synapsify NLG platforms require significant investment in time and capital resources to implement, including the effort to create the corpus of utterances and how they will be applied in an application. Some solutions contain prebuilt corpuses and libraries of phrases and utterances, so the overall cost can range from tens of thousands to hundreds of thousands of dollars. Ecosystem Business value-add, adjusted for uncertainty Time to reach next Trajectory (known or prospective) Creation Low 3 to 5 years Significant success Unlike the older natural language processing (NLP), NLG is a work in progress. The generation of natural text requires large libraries of utterances or preprogrammed factoids to execute properly. NLG is a harder problem to solve than NLP, and thus this technology lags behind its more mature cousin. Right now, these platforms are creating low value due to the time and effort required for AD&D pros to train them. However, it is possible to get greater value-add from these platforms in scenarios where simple structured data is wrapped with NLG in a highly scalable way. NLG will reach the growth over the next five years as the attention increases for use in the areas of customer service, textual report analysis, and business intelligence insights. NLG has seen success in the large-scale automation of simple textual content, but most tools and approaches have required significant technical expertise or come with a high cost. 12

14 FIGURE 7 TechRadar : Creation Phase, Swarm Intelligence, Q1 17 Element Definition Usage scenarios Vendors Estimated cost to implement Categorization (if applicable) Explanation Swarm intelligence technologies are decentralized systems with many individual contributors that coordinate and self-organize to produce emergent intelligence. This technology uses the collective behavior of biological swarms, such as flocks of birds, schools of fish, and social insects like bees and ants. Swarm intelligence is applied both to swarms of software agents acting in coordination to solve problems as well as to groups of humans that are all collectively contributing to a decision (done through a web or mobile application). Swarm intelligence is used to optimize routing, such as for trucking and delivery fleets; provide distributed intelligence in experimental robotics; and support collective/emergent decisions when applied to leveraging the collective intelligence of large groups of humans. AxonAI Swarm intelligence is still an emerging field, and implementations require significant bespoke work, driving the cost to implement into the hundreds of thousands of dollars, including development and support from outside consultants. Ecosystem Business value-add, adjusted for uncertainty Time to reach next Trajectory (known or prospective) Creation Negative 5 to 10 years Minimal success While the principles of swarm intelligence have been around for decades, the application has largely been limited to robotics; even today, the number of companies using it in software is limited. While there is potential to improve many complex analysis areas using swarm intelligence, such as Monte Carlo simulations, the rate of adoption and experimentation keeps this technology in the creation. Because of the limited adoption, limited understanding of methods and principles, and sparse vendor landscape, the business value-add for this technology is a net negative. Swarm intelligence is significantly less well known as a method for building machine intelligences, and a significant amount of research and prototyping must be executed before this technology can enter the next, keeping it five to 10 years away. Due to the effort needed to advance this technology to the next, and the possibility that it could continue to be overshadowed by other technology priorities, the trajectory is only on minimal success. 13

15 Survival: Cloudenomics And Rich Tools Give Developers Power To Sense And Think Typically, in the Survival, the first commercial and open source products hit the market, initial production environment deployments take place, and the ecosystem expands to include suppliers, customers, and enablers like systems integrators. Many technologies in the Survival have existed for years or decades but are now increasing in business value to a broader set of companies because of scalable processing and storage from cloud infrastructures, wider availability of the data sets to build models, and tools making obscure technologies available. More mature solutions are included when, for one reason or another, they are struggling to make it to the Growth. We ve placed four technologies in the Survival : Biometrics. Biometrics measures the characteristics of human expressions and physical states to understand intent, emotion, age, etc. (see Figure 8). Today, biometrics applications can capture humans facial expressions or body language to detect engagement levels or emotional responses (e.g., in-store applications providing a personalized experience on a digital display). Currently most of the applications using biometrics are for post hoc analysis and modeling, but we are seeing an increasing number of vendors make this available for immediate real-time interactions within applications. Image and video analysis. Thanks to the increasing availability of cloud-based image and video analysis platforms from major vendors and startups alike, companies from the retail, insurance, market research, and security industries are now taking advantage of the insights in video feeds, marketing content, and other image data sources (see Figure 9). A minor level of investment can create applications that determine foot traffic. Or these apps can identify customers age and gender for content targeting, comprehend some human emotions through facial expressions, or identify suspicious actors to both reduce risk and enhance customer engagement and experience. Semantic technology. A central problem of AI is getting it to comprehend its environment and context to make decisions, and this requires a deep understanding of data (see Figure 10). 21 Semantic technology provides that context for AI systems by analyzing data and creating a knowledge base of classifications, ontologies, taxonomies, hierarchies, data models, relationships, and metadata. 22 AI uses this knowledge base as a way to understand its world and drive decisions about execution. Semantic technology is a broad category in itself and includes aspects of master data, machine learning, metadata management, and data modeling. Speech recognition. Speech recognition and speech-to-text transcription are other areas where the scalability in data sets, processing, and analysis have put previously obscure and expensive technology into the hands of enterprises (see Figure 11). Customer service IVR has been leveraging this technology for years, as anyone who called a support line in the past decade knows. 23 Mobile applications, where a hands-free interaction is often preferred, have begun to take advantage of this technology and are seeing significant adoption from users. 24 We expect it to grow as more tools and services offer these capabilities to developers to more easily integrate into applications, facilitating a smooth and naturalistic interface. 14

16 FIGURE 8 TechRadar : Survival Phase, Biometrics, Q1 17 Element Definition Usage scenarios Vendors Estimated cost to implement Categorization (if applicable) Explanation Biometrics technologies enable more naturalistic interactions between humans and machines, including but not limited to image and touch recognition, speech, and body language. The use of this technology varies widely today, depending on the type of human recognition involved. Speech recognition has progressed to the point where firms leverage it in consumer and business scenarios (e.g., Amazon Alexa, Apple Siri, Google Now, and Microsoft Cortana), while emotional recognition of facial expressions and body language is still largely limited to market research and other more controlled situations. 3VR, Affectiva, Agnitio, Amscreen, Aurora Technologies, FaceFirst, KeyLemon, Linkface, ModiFace, Nuance Communications (IBM Watson Developer Cloud, HPE Haven OnDemand, Microsoft Cognitive Services), Sensory, Synqera, and Tahzoo The cost to implement is high due to the effort required to train the models successfully. Several vendors in this space enable their clients to fully own all facial data collected, ensuring better data security and privacy but slowing the rate at which the costs to implement will come down (due to vendors having to build their own data sets to feed models). Companies using biometrics should account for some time to train models, particularly if they have no historical data or have niche requirements. Ecosystem Business value-add, adjusted for uncertainty Time to reach next Trajectory (known or prospective) Survival Low 3 to 5 years Moderate success Right now it is unclear to what degree consumers are willing to be identified. Identifying customers for their own security is one thing, but identifying them to serve them advertisements is another. The adoption of biometrics by AD&D pros will depend on their ability to respect customers privacy while delivering added convenience. Biometrics is providing value today in market research and niche scenarios, but it will need to increase accuracy, drop in cost, and broaden its applicability to understand human body language and provide more significant business value. At the current rate of vendor and end user model development, we expect to see this technology advance to the growth within three to five years. There is a distinct possibility that not too far in the future, biometrics will be ubiquitously used to identify customers, detect emotional states, and predict behavior. However, over the next decade, AD&D pros using this technology will continue to struggle to build large-enough data sets for highly accurate models and may also encounter issues with privacy and personal security in the application of these models. 15

17 FIGURE 9 TechRadar : Survival Phase, Image And Video Analysis, Q1 17 Element Definition Usage scenarios Vendors Estimated cost to implement Categorization (if applicable) Explanation Image and video analysis comprises tools and technology to analyze images and video in order to understand and interpret the objects and object features within the images and video. Image and video analysis is currently used in a range of scenarios across the public and private sectors, including fraud and risk analysis (for law enforcement and insurance), customer behavior patterns for in-store digital experiences, and media content analysis. Clarifai, Deepomatic, Google (Cloud Platform APIs), Hewlett Packard Enterprise (Haven OnDemand), HyperVerge, IBM (Watson Developer Cloud), indico, Microsoft (Cortana Intelligence Suite), and Talkwalker The cost to implement depends heavily upon whether you are training your own models or leveraging some of the prebuilt models available from various vendors. Training your own can be very expensive, ranging into hundreds of thousands or millions of dollars to build the data sets needed and train the models to a level of acceptable accuracy. Ecosystem Business value-add, adjusted for uncertainty Time to reach next Trajectory (known or prospective) Survival Low 1 to 3 years Moderate success While image and video recognition has enjoyed success in specific use cases such as medical diagnostics, object detection in manufacturing quality control, and brand protection (in copyright), it still has some ways to go until a wide swath of enterprises adopt it for more advanced use cases, such as content analysis. Given the expense of training models and lack of best practices for implementation, image and video analysis is delivering low value when viewed across all use cases. Forrester expects a rapid evolution for image and video analysis. As these tools continue to learn, they will become more accurate and require less initial customization. Best practices for classifying image data will also begin to emerge and support the business case for adoption. Forrester believes that image and video analysis will eventually automate tag management for visual assets and will add a new layer of depth to customer, content, and physical asset understanding. 16

18 FIGURE 10 TechRadar : Survival Phase, Semantic Technology, Q1 17 Element Definition Usage scenarios Vendors Estimated cost to implement Categorization (if applicable) Explanation Semantic technology is used to digitally represent knowledge for AI systems to understand their world in context using semantic graphs, ontologies, libraries, metadata, or rules. The simplest example: Semantic technology can represent relations between words (such as identifying the word queen as a synonym for king because of it being a female modifier of male ruler ), but true knowledge representation goes beyond this simple thesaurus referencing and ties together different pieces of data because of like attributes and metadata (such as friendships or other hidden relationships). Used to feed intelligent systems with the context to produce understanding and drive actions in the real world, semantic technology can help virtual agents understand concepts represented in different types of queries, support the execution of complex business processes using rules and probabilistic models, and provide the context for emerging scenarios like providing field technicians with the right technical manual to fix a wind turbine, oil derrick, or soda machine. Alation, Coveo, Creative Virtual, Cycorp, Earley Information Science, Expert System, Loop AI Labs, and Ontotext Successful implementation of semantic technology can cost hundreds of thousands of dollars due to a combination of employee time and investments in software and deployment. Also, the need to support these solutions with ongoing curation and updating adds to the overall implementation cost. Ecosystem Business value-add, adjusted for uncertainty Time to reach next Trajectory (known or prospective) Survival Low 5 to 10 years Moderate success While these technologies have existed in various forms for years, many companies are still not thinking about this piece of the AI picture when building their systems and utilizing the platforms to do so keeping this in the survival. Enterprises that successfully implement semantic technology can see returns in their ability to build more dynamic, contextually aware, and intelligent systems. Because of the slow rate of adoption, this technology will not reach the growth for another five to 10 years. This technology is on a trajectory of moderate success. However, this could move to significant success if the market and buyers hit an inflection point where they realize the importance of semantic technologies in making AI systems more robust and automated. 17

19 FIGURE 11 TechRadar : Survival Phase, Speech Recognition, Q1 17 Element Definition Usage scenarios Vendors Estimated cost to implement Categorization (if applicable) Explanation Speech recognition tools understand and interpret the spoken word; this technology captures audio signals and transforms them into written text and other data (such as emotion or intent) that is then used to drive application functionality. Speech recognition is used in a widening array of scenarios, from interactive voice response (IVR) systems in customer service to voice queries on a mobile phone or a physical robot. Speech recognition is used to understand human speech, transcribe that speech, and transform it into a usable format for applications and analytics. Calabrio, NICE, Nuance Communications, OpenText, and Verint Systems The cost to implement speech recognition is high due to the data needed to train these tools. A number of vendors provide products or services with prebuilt models that can significantly drive down the cost, and we expect the overall cost of these platforms to drop over time as more training data becomes available to enterprise end users. Ecosystem Business value-add, adjusted for uncertainty Time to reach next Trajectory (known or prospective) Survival Medium 3 to 5 years Significant success While IVR systems that convert speech to text have been around for over a decade, more advanced use cases and techniques (such as understanding vocal sentiment and emotion) are still emerging. Particularly, the use of voice-based interactions on mobile devices is still an emerging application, although we expect this to be the main driver of speech analytics use cases (along with interacting with embedded software in internet-of-things devices) over the coming years. The business value-add for speech analytics in the form of IVRs has been significant over the past 15 years, but the value-add for mobile queries has yet to be fully proven. We expect this to rise before the technology reaches the next. At the current rate of vendor and end user model development, we expect to see speech recognition technology advance to the growth within three to five years. Companies using these platforms have seen significant success in their adoption by users, both in IVR scenarios for customer service and support as well as in mobile interactions driven by voice. 18

20 Growth: Core AI Technologies Have Proven Value, But Adoption Friction Is High The Growth includes technologies that have reached a level of diversity and resilience that sustains their existence. A growing number of implementations produce a solid body of evidence about the technology s value that allows potential customers to make better-informed decisions. These powerful technologies are still complex, however, and may require significant investment in time and resources to deploy. We ve included five categories in the Growth : AI-optimized hardware. Central processing units (CPUs) are not the most efficient hardware for processing much of the math to build the models that AI uses as the core of its intelligence a key reason that has hindered the progress in the development of practical AI applications (see Figure 12). Graphics processing units (GPUs) have come to the fore in recent years as the optimal way for executing many AI tasks, particularly training with machine and deep learning. And now some users are finding that there is an increasing focus on appliances for building models for AI, which take advantage of networking optimizations between GPUs and clusters. In addition to providing hardware, vendors are attempting to accelerate the innovation of researchers and developers in AI by offering toolkits, frameworks, and software starter packages for deep learning. Machine learning platforms. Machine learning can solve myriad problems: predicting maintenance schedules or customer churn, identifying fraud or new customer prospects, or delivering relevant content to employees and customers (see Figure 13). While there is great value in machine learning, it still requires building specific models from well-prepared data sets to produce specific predictions. Cloud-based platforms offering machine learning model-as-a-service for common use cases mitigate some of these challenges. In addition to an increasing range of commercial vendor tools, there is also a rich open source ecosystem for machine learning. 25 While powerful, however, machine learning is likely to lag behind deep learning for many use cases because the latter can be applied more flexibly. Robotic process automation. Robotic process automation (RPA) provides enterprises with a way to improve an as-is process without engaging in a large process redesign effort and technical tasks (such as data integration) (see Figure 14). 26 The RPA bot mimics the activities of a human (such as clicking, dragging, or typing) that are too expensive or inefficient for a human to execute. They include bank onboarding (which may require data entry into dozens of enterprise systems) or testing and routine maintenance activities for IT service management. 27 Firms can train RPA solutions by directly recording a human executing a process and then using that as a starting point for macro development. RPA will fit into larger AI systems and can support significant scaling of human labor and expertise. Text analytics and NLP. Text analytics has come a long way from simple word counting and topic extraction and now includes more complex capabilities that extract sentiment or process natural human language in real time, from either typed text or whole documents (see Figure 15). 28 Furthermore, you can classify words and parts of speech, apply a combination of linguistic rules and statistical methods, and analyze relationships. NLP uses and supports text analytics by 19

21 facilitating the understanding of sentence structure and meaning, sentiment, and intent through statistical and machine learning methods. NLP is significantly more advanced in both science and tooling than NLG. Firms are using it in a broad set of deployments from search interfaces to customer service, automated assistants in the form of virtual agents or chatbots, and fraud detection and security. Virtual agents. Virtual agents are the current darling of the media they range from simple chatbots to advanced systems that combine many other pieces of the AI technology landscape to produce agents that can network with humans (see Figure 16). 29 This software interacts through various modes such as voice, desktop chat, or mobile messaging. At the high end, it can also interact with enterprise systems to accomplish meaningful tasks such as entering or updating records in a CRM and reading or writing on transactional databases. Companies are seeing significant value in customer experience improvements even when they apply the technology to much narrower scenarios such as telling customers what their operating hours are

22 FIGURE 12 TechRadar : Growth Phase, AI-Optimized Hardware, Q1 17 Element Definition Usage scenarios Vendors Estimated cost to implement Categorization (if applicable) Explanation AI-optimized hardware comprises graphics processing units (GPUs) and appliances specifically designed and architected to efficiently run machine learning, deep learning, and other AI-oriented computational jobs. These platforms can optimize the processing of many algorithms required for AI, particularly deep learning algorithms running on GPUs. Additionally, appliance vendors further optimize this by providing efficient communication and data movement for processing. GPUs are used to run a variety of machine and deep learning algorithms; their architecture allows them to process the computations in parallel and build models much more efficiently. Also, a variety of cutting-edge methods (such as computationally simulated neurons) promises to further optimize the calculations needed to build the models powering AI and cognitive intelligence. Alluviate, Cray, Google, IBM, Intel, and Nvidia (GTX) AI-optimized hardware costs can be high, particularly for organizations that are building their own large internal capabilities for training and running their own deep learning models. Single cores of AI-optimized hardware can run close to $10,000, and implementations may require scores of cores. Ecosystem Business value-add, adjusted for uncertainty Time to reach next Trajectory (known or prospective) Growth High 5 to 10 years Significant success The realization of the suitability of GPUs for running deep learning algorithms is driving tremendous growth in this space, both within enterprises and at cloud service providers that are buying massive amounts of AI-optimized hardware to build their own systems and offer AI capabilities as-a-service. The use of GPUs in training deep learning algorithms has accelerated the speed of modeling and the implementation of these into applications. This is allowing more companies to gain the deeper insights provided by deep learning algorithms and to realize the differentiated value that they can provide. AI-optimized hardware is still too expensive for many enterprises to adopt at scale internally, but external adoption is significant today. The growth of this technology will continue over at least the next decade, when we expect that AI-optimized hardware will be ubiquitously embedded within most hardware systems. AI-optimized hardware is driving significant success in the application of deep-learning algorithms that are essential for pushing forward the advancement of AI technologies. 21

23 FIGURE 13 TechRadar : Growth Phase, Machine Learning Platforms, Q1 17 Element Definition Usage scenarios Vendors Estimated cost to implement Categorization (if applicable) Explanation Machine learning platforms provide algorithms, APIs, development and training toolkits, data, as well as computing power to design, train, and deploy models into applications, processes, and other machines. Popular algorithms include regressions, decision trees, Bayesian models, and unsupervised clustering methods as well as more traditional statistical models. Machine learning platforms are used in a wide variety of scenarios to solve specific, relatively narrow problems that require recognizing patterns across large or complicated data sets. In security and risk, they are used to identify fraud and money laundering; in digital experiences, they are used to develop customer segments for personalization and targeting; in finance, they are used to develop forecasts and predictors of market activity. Amazon Web Services (Machine Learning), Fractal Analytics, Google (Cloud Platform Machine Learning APIs), H2O.ai, IBM (Watson Developer Cloud), Microsoft (Cortana Intelligence Suite), SAS, and Skytree While many open source machine learning platforms are available, the cost to successfully implement them and produce useful models can range into the hundreds of thousands or even millions of dollars due to the need to train them on large, clean data sets and the time needed to experiment with several different models before deploying into production. Using prebuilt models from cloud-based platforms can be much more cost-efficient (costing as low as a few thousand dollars for using prebuilt models for small batch jobs), but the range of problems that these can solve is narrower. Ecosystem Business value-add, adjusted for uncertainty Time to reach next Trajectory (known or prospective) Growth Medium 5 to 10 years Significant success Many of the machine learning algorithms in these platforms have been around for decades, but the increasing availability of the compute power to build models and the libraries and platforms for them has put this technology into the growth. Machine learning platforms allow application developers to infuse intelligence into a wide variety of scenarios, but because each model can only be applied to narrow scenarios and needs significant data preparation, the business value-add can be diluted. These platforms and the algorithms they offer are widely adopted in many verticals, such as telecommunications and financial services. Other verticals will continue to adopt them before machine learning platforms reach the stage of equilibrium. As companies expand machine learning from customer insights into product development, operations, and other facets of the organization, these platforms will continue to enjoy significant success. 22