Vertex perspectives artificial intelligence
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The document summarizes the evolution of artificial intelligence (AI) from the 1950s to the present. It discusses three waves of AI development: handcrafted knowledge in the early period, statistical learning from the 1960s to 1980s, and contextual adaptation from the 1990s onward. Recent advances are driven by increased computing power, data availability, and new algorithms. Deep learning is increasingly important and applications include voice control, natural language processing, and computer vision. While AI has great potential, a lack of talent and data is creating a bifurcated ecosystem with large tech firms at the top.
Vertex perspectives artificial intelligence
- 2. The evolution of Artificial Intelligence (AI) since the 1950s went through several phases……. Source: Artificial Intelligence by Giorgio Fumera from University of Cagliari | AI: 15 Key Moments in the Story of Artificial Intelligence by BBC | Future Progress in Artificial Intelligence: A Survey of Expert Opinion Further Advancement (Mid 1980s–1990s) Great Expansion (Mid 1960s–early 1980s) Recent Developments (Mid 1990s – ) Starting from solving problems with toys and real-world situations including game playing, proving theorems, natural language processing (NLP), recognizing objects in images Research funding from DAPRA’s strategic computing program in US, Fifth Generation Computer System in Japan and ESPRIT in Europe Technical and theoretical advances including the rise of machine learning Many successful commercial applications including: games, robots, driverless vehicles, homes, recommender systems, automated trading, translating systems, aircraft autopilots, fraud detectors, search engines, etc. Early Explorations (1950s–1960s) Alan Turing introduced the Turing test “Artificial Intelligence” was created at Dartmouth conference 1956 1950 1966 1969 Shakey mobile robot MIT’s “summer vision project” 1973 AI winter 1981 AI’s commercial value started to realize 1990 Rodney Brooks was inspired by advances in neuroscience, which had started to explain the mysteries of human cognition 1997 IBM’s Deep Blue defeated the world chess champion 2002 iRobot - first home robot 2011 IBM’s Watson won "Jeopardy!“ 2012 Google Brain recognised a cat from millions of unlabeled videos 2016 Google's AlphaGo defeated the Go world champion The Future AI experts estimate a 90% chance of machines achieving human-level intelligence by 2075 and superintelligence within 30 years of attaining human- level intelligence
- 3. That can be clustered into three waves of AI development that may be described as - handcrafted knowledge, statistical learning and contextual adaptation Source: A DARPA Perspective on AI Abstracting Perceiving Learning Reasoning 2 Statistical learning Systems based on statistical models developed to address specific challenges and trained using big data • Examples: voice recognition, face recognition • Features: Nuanced classification and prediction capabilities. No contextual capability and minimal reasoning ability • Challenges: statistically impressive but individually unreliable, inherent flaws can be exploited, skewed training data creates maladaptation, “blackbox” AI Wave 1 Handcrafted Knowledge Systems that have established sets of rules to represent knowledge in well-defined domains • Examples: logistics program scheduling, game-playing programs • Features: Enables reasoning over narrowly defined problems. No learning capability and poor handling of uncertainty • Challenges: The structure of the knowledge is defined by humans. The specifics are explored by the machine. Failure of the autonomous cars in the DARPA Grand Challenge 3 Contextual Adaptation • Systems that construct contextual explanatory models for classes of real world phenomena • Examples: Image recognition • Features: Ability to perceive, learn, abstract and reason • Models that generate explanations of how an object might have been created to explain and drive decisions
- 4. These waves of AI developments are advancing rapidly, driven by machine and deep learning Faster and more powerful computation (GPUs) Greater data availability Development of new algorithms Tech giants are opening up resources to enable others to develop better AI (e.g. TensorFlow, Amazon AI) Availability of cloud-based infrastructure Factors driving the rapid advancement of AI Source: The Fourth Industrial Revolution: a Primer on Artificial Intelligence by David Kelnar Human intelligence exhibited by machines Focal Areas of AI • Reasoning • Knowledge • Planning (including navigation) • Natural language processing • Perception Statistical techniques enable predictions by machines to improve with experience Beyond deep learning, it includes various approaches: • Random forests: create multitudes of decision trees to optimise a prediction • Bayesian networks: use a probabilistic approach to analyze variables and the relationships between them • Support vector machines: be fed categorized examples and create models to assign new inputs to one of the categories • It models the brain and uses an artificial ‘neural network’ - a collection of neurons connected together • It is useful because the algorithm undertakes the tasks of feature specification (defining the features to analyze from the data) or optimization (weighing the data to deliver an accurate prediction) Artificial Intelligence Deep Learning Machine Learning A subset of AI A subset of machine learning The broadest term 1950's 1960's 1970's 1980's 1990's 2000's 2010's
- 5. • Deep learning uses neural networks loosely analogous to the observed behaviour of a biological brain's axons • It consists of multiple hidden layers of networks between the input and output layers and are trained separately, breaking down the characteristics of the data into multiple parts and combining all the layers in the end to provide the output Trend 1: Looking ahead, deep learning is expected to be an important technique in AI Input layer: data can be fed into the network Hidden Layer: information is processed Output Layer: results come out Source: Computer Science: The Learning Machines by Nature | The Fourth Industrial Revolution: a Primer on Artificial Intelligence by David Kelnar | From not Working to Neural Networking by The Economist | Analytics Vidhya | Artificial Intelligence: 10 Trends to Watch in 2017 and Beyond by Tractica | Learning Deep Architectures for AI By Yoshua Bengio Neural networks Illustration of using deep learning neutral networks for facial recognition 1. Identify pixels of light and dark 2. Learn to identify edges and shapes 3. Learn to identify more complex shapes and objects 4. Learn which shapes and objects define a human face Deep Learning Characteristics • Deep learning helps reduce the time and effort spent on feature engineering. Deep learning is increasingly used in conjunction with machine learning, natural language processing (NLP), computer vision or machine reasoning • Deep learning is typically employed for feature extraction on a larger or more complex set of data while employing machine learning algorithms to perform basic clustering or regression learning tasks when features of the data have been determined • The performance of deep learning neural networks (DNN) has been demonstrated to increase on a linear scale with the increase in number of DNN layers, necessitating hardware to process and train these algorithms to also grow in scale Image source: Andrew Ng
- 6. Trend 2: There are different types of learning, with semi-supervised and reinforcement learning gaining traction Source: Three Things You Need to Know About Machine Learning by Medha Agarwal | Machine Learning by TechJini • Given pre-determined features and labeled data • Direct feedback • Predict outcome • Example: traditional insurance underwriting • Challenge: need for large amount of labeled data which is costly and time-consuming Supervised Semi- Supervised Unsupervised Reinforcement LearningTypes • Given unlabelled and unstructured data • No feedback • Find hidden structure • Example: customer segmentation • Challenge: tend to be less accurate • A blend of supervised and unsupervised learning • Used for situations in which there is some labelled data but not a lot • Example: Gmail spam • It is expected to see increasing usage for large data sets, where data labelling is an issue • Experience driven sequential decision-making • Occasional feedback in the form of a reward • Example: Game (AlphaGo), robots, autonomous driving • Challenge: require a significant amount of data • Algorithms with fewer layers. For instance, logistic regression, support vector machine • Better for relatively less complex and smaller datasets • New technique that uses many layers of neural networks • Useful for complex target function and large datasets LearningTechniques Shallow Deep
- 7. Source: Expect Deeper and Cheaper Machine Learning by IEEE Spectrum | How AI is Shaking up the Chip Market by Wired | A Machine Learning Landscape by Karl Freund from Moor Insights & Strategy | Artificial Intelligence: 10 Trends to Watch in 2017 and Beyond by Tractica Trend 3: Shift from GPUs to AI optimized hardware • Graphics processing units (GPUs) have been the dominant hardware platform for AI applications and are expected to drive advances in performance, especially for high-performance deep learning systems • At the same time, the emergence of alternative hardware platforms like field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), and specialized processor architectures are competing with GPUs on performance, cost, and power consumption • As AI algorithms advance to account for applications with dynamic inputs (e.g. autonomous driving, personalized medicine), the evolving nature of algorithms and workloads will determine suited architecture. Processors will increasingly be “right-sized” to align capabilities and cost with specific workloads. Hardware across the Machine Learning Landscape There are two key aspects: Training refers to training the neural network with massive amounts of sample data. It is typically performed in large datacenters on GPUs, almost exclusively provided by NVIDIA for the time being. Inference refers to using a trained model to provide outputs on real-world data. • It is usually done at the application or client end point, rather than on the server or cloud • Inference requires fewer hardware resources, and depending on the application, can be performed using a central processing unit or non-specialized hardware. This could be FPGA, ASICs, digital signal processor, etc • There are rising expectations that inference will move locally to mobile devices Image source: Moor Insights & Strategy
- 8. • Rapid advances in machine learning and NLP have also enabled voice control to become more practical • Companies like Baidu, Apple, Google, Microsoft, and Amazon are making significant progress in voice recognition using DNNs • Voice control may soon be sufficiently reliable for interacting with an array of devices, robots, and home appliances coupled with open source But some challenges remain: • The complexity, subtlety and power of language • An analysis of the way people use Alexa and Google’s assistant platforms shows that third-party apps have not been well used nor particularly sticky In the past Now & Future Developers published over 10,000 skills on Amazon Alexa by 23 Feb 2017 Improvement of word accuracy rates by platform 950 1000 1400 2000 3000 4000 5191 6068 7053 10000 2016 May 2016 Jun 2016 Jul 2016 Aug 2016 Sep 2016 Oct 2016 Nov 2016 Dec 2017 Jan 2017 Feb Trend 4: Machine learning and natural language processing give rise to voice as the next conversational interface Source: Amazon Alexa Now has 10k Skills, Including Europe by Voicebot.ai | MIT Technology Review Image source: Internet Trends 2016 by KPCB
- 9. Generalized Intelligence: broad mental capacity that influences performance on cognitive ability measures (Examples: emotional intelligence, creativity, intuition) Reasoning: solving problems through logical deduction (Examples: legal assessment, financial asset management, games) Knowledge: representing knowledge about the world. (Examples: medical diagnosis, drug creation, fraud prevention) Planning: establishing and achieving goals (Examples: logistics, physical and digital network optimization, predictive maintenance) Communication: understanding written and spoken language (Examples: voice control, real-time translation and transcription) Perception: deducing things about the world from visual images, sounds, and other sensory inputs (Examples: autonomous vehicles, medical diagnosis, surveillance) 1 2 3 4 5 6 Goals of AI Reasoning Knowledge PlanningPerception Generalized Intelligence Communication 1 2 3 4 5 6 AI Challenge Resolution Capabilities Trend 5: AI is especially important given its ability to solve challenging problems and potentially impact almost every industry Source: The Fourth Industrial Revolution: a primer on AI by David Kelna | AI, Deep Learning, and Machine Learning: A Primer by Frank Chen Horizon Robotics uses large-scale cloud-based deep neural network algorithms on high- performance and low-power brain processing units for applications in smart homes and autonomous cars (www.horizon-robotics.com) Dynamic Yield delivers an end-to-end platform for personalization in eCommerce, media, travel industry using machine learning to improve targeting capabilities (www.dynamicyield.com) Xuebajun is a mobile application that helps students solve homework questions using Scene Text Recognition (STR) Technology and deep learning to improve character recognition rate (www.xueba100.com) LightCyber uses machine learning to map out and monitor all users and devices on a company network. It detects behavioural anomalies and selects meaningful actionable alerts for escalation (www.lightcyber.com) Maxent provides anti-fraud software as a service based on machine learning techniques (www.maxent-inc.com) Selected Vertex Portfolio Companies
- 10. Firms Talent | Data Trend 6: At the same time, a dearth of talent and data is driving the emergence of a bifurcated AI industry ecosystem that is top- heavy and long-tailed Dominant Players | Technology & Financial Services Companies • Have access to very large training datasets and the ability to drive the advancement in algorithms • Focus on highly scalable use cases like image recognition or patient data processing, which avail the most significant revenue opportunities or help enhance existing services and products to gain a competitive edge • Be expected to lead the top-heavy AI ecosystem because of specialty in high performance computing systems that power advanced use cases such as predictive maintenance, algorithmic trading or static image recognition A dearth of talent and data in AI are critical challenges that play to the strengths of dominant players like big technology firms and financial service companies creating a bifurcated AI industry ecosystem comprising: Small & Medium-sized Enterprises (SMEs) | Startups • Possess relatively smaller data sets • Focus on using or enhancing existing algorithms (many are increasingly open-sourced) as well as employing high- performance cloud services • There are many niche applications where AI is expected to add value and startups can compete effectively with the bigger players Source: Artificial Intelligence: 10 Trends to Watch in 2017 and Beyond by Tractica | Artificial Intelligence is the New Electricity by Andrew Ng | Banks and Tech Firms Battle Over Something Akin to Gold: Your Data by the New York Times| JPMorgan Software Does in Seconds What Took Lawyers 360,000 Hours by Bloomberg | Wells Fargo Increases Emphasis on Emerging Technologies by Wells Fargo
- 11. Final Comments Vertex has invested in companies across geographies addressing different industry applications leveraging AI to transform their service offerings. These include Xuebajun in education, Horizon Robotics in autonomous cars and smart homes, Maxent in fraud detection, Dynamic Yield in customer personalization and optimization, and LightCyber in cybersecurity (recently acquired by Palo Alto Networks). With each investment, we’re learning more about success strategies using AI to transform industries. We’re excited to be active investors in this space and are looking forward to the journey ahead.
- 12. Thanks for reading! Disclaimer This presentation has been compiled for informational purposes only. It does not constitute a recommendation to any party. The presentation relies on data and insights from a wide range of sources including public and private companies, market research firms, government agencies and industry professionals. We cite specific sources where information is public. The presentation is also informed by non-public information and insights. Information provided by third parties may not have been independently verified. Vertex Holdings believes such information to be reliable and adequately comprehensive but does not represent that such information is in all respects accurate or complete. Vertex Holdings shall not be held liable for any information provided. Any information or opinions provided in this report are as of the date of the report and Vertex Holdings is under no obligation to update the information or communicate that any updates have been made. About Vertex Holdings Vertex Holdings, a member of Temasek Holdings, focuses on venture capital investment opportunities in the information technology and healthcare markets, primarily through our global family of direct investment venture funds. Headquartered in Singapore, we collaborate with a network of global investors who specialize in local markets. The Vertex Global Network encompasses Silicon Valley, China, Israel, India, Taiwan and Southeast Asia. Contact us: Brian Toh btoh@vertexholdings.com Tracy Jin tjin@vertexholdings.com James Lee jlee@vertexholdings.com