Artificial intelligence as a business technology

Artificial intelligence as a business technology
A ciopages.com Concept briefing

TABLE OF CONTENTS EXECUTIVE SUMMARY WHAT IS ALL THIS “AI” BUZZ ABOUT?
What Is AI? | What’s The History Of AI? | AI Is Booming | Why is AI Booming? THE CURRENT STATE OF AI AI Is Everywhere | How Are Businesses Adopting AI? | What Does The AI Vendor Landscape Look Like? | Making Sense Of AI Vendor Landscape ADOPTING AI IN YOUR BUSINESS Technical Considerations/Challenges | “People” Considerations/Challenges | Building Your AI Team: Considerations/Challenges/Composition THE FUTURE OF AI IN BUSINESS

Executive summary

Executive summary (1/3) “The pace and potential of AI mean it is not something we can afford to ignore. It is perhaps the single most important area of decision-making business leaders will face over the next few years. The depth of a CIO’s understanding could be the crucial differentiator between success and failure of firms in a fast-changing world." WHAT’S THE FUSS ABOUT? From security to blockchain technology and 3D printing, every new technology development comes with exhortations for CIOs to place it top of their agenda. However, in the case of AI, it may well be the most important change we’ll see in the philosophy, practice and management of business. AI draws on and is combining with exponential performance developments in technologies such as computer hardware, big data management, the internet of things and the fields of machine-learning, neural networks, and robotics. As a result, AI is beginning to fulfill its potential of transforming businesses. CIOs have to ensure they are investing the time and attention to understand what AI is, why so much is being invested and where the opportunities are. Credits/References: Raconteur

Executive summary (2/3) WHAT’S ITS POTENTIAL?
The place to start is to educate management about its potential, and undertake internal analysis of where it could be deployed and what competitors are doing. Medium to large enterprises in particular are bringing in AI experts to take a broader perspective of the potential roles it could play from smarter production management to customer targeting and broad-based decision-making. HOW FAST IS IT MOVING? The pace of AI development has caught most unaware. We are beginning to understand the scale of the investment being made by companies such as Google, IBM, Microsoft, Uber and Baidu. Many of the biggest developments and research projects are kept under the radar until launch, and we can only speculate on what we might see next, from robot lawyers to ultra-intelligent personal assistants. WHO SHOULD LEAD? Some companies are making it the responsibility of the chief executive, chief operating officer, CIO, or business transformation head to drive the identification, piloting and application of AI solutions across all aspects of the business. Credits/References: Raconteur

Executive summary (3/3) HOW DEEP SHOULD WE TAKE IT?
Many firms are looking at relatively narrow deployments to automate rule-based decision-making, and predict future demand and customer behavior from accumulated data. Others are looking at much broader deployments such as intelligent human resources, finance and legal advisers, and real-time data analytics of live transactions. Deployment of AI could lead to deep insights into the potential behavior of employees, customers and partners. What about all this talk of AI taking over the world? Futurist, inventor and author Ray Kurzweil, has predicted that in the coming decades, machines will be able to simulate human intelligence, a phenomenon he calls the Singularity. People talk about AI taking over, and that is not the case. We are so far from any world where that’s a reality. Machines will get smarter over time, experts say, but the progress will be gradual. All in all, AI is going mainstream, but no “Singularity" in sight. WHAT WOULD SUCCESS LOOK LIKE? “Fail fast and cheap” – bringing in suppliers, customers and other value-chain partners early on to see if there is commercial merit in an idea. There can be as much learning from a failed project as a successful one. Credits/References: Raconteur

What is all this “ai” buzz about?

What is ai? (1/3) AI has become a buzzword. It is often used alongside terms like Machine Learning, Deep Learning, Neural Networks etc. It is important to understand how these terms are interrelated. COMPUTER SCIENCE (CS) The study of automating algorithmic processes that scale. A computer scientist specializes in the theory of computation and the design of computational systems Artificial Intelligence (AI) Artificial intelligence (AI) is intelligence exhibited by machines. An ideal "intelligent" machine is a flexible rational agent that perceives its environment and takes actions that maximize its chance of success at some goal Machine Learning (ML) The study and construction of algorithms that can learn from and make predictions on data DEEP LEARNING (DL) Deep learning is a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data by using a deep graph with multiple processing layers, composed of multiple linear and non-linear transformations. Credits/References: Arno Candel, H2O.ai

What is ai? (2/3) The artificial intelligence ecosystem is complex. It runs from machine learning to natural language processing and generation to predictive analytics to deep learning systems! Credits/References: NarrativeScience

What is ai? (3/3) Deep Learning is probably the hottest area in AI today. CIOs can’t afford to ignore it! Deep Learning explained with an example A short yet very powerful video about Deep Learning Credits/References: Arno Candel, H2O.ai

What’s the history of ai? (1/3)
The promise of intelligence The quest for artificial intelligence (AI) began over 70 years ago, with the idea that computers would one day be able to think like us. Ambitious predictions attracted generous funding, but after a few decades there was little to show for it. But, in the last 25 years, new approaches to AI, coupled with advances in technology, mean that we may now be on the brink of realizing those pioneers’ dreams. 1950 Science fiction steers the conversation: In 1950, I Robot was published – a collection of short stories by science fiction writer Isaac Asimov. Asimov was one of several science fiction writers who picked up the idea of machine intelligence, and imagined its future. 1956 A 'top-down' approach: The term 'artificial intelligence' was coined for a summer conference at Dartmouth University, organized by a young computer scientist, John McCarthy. Top scientists debated how to tackle AI. 1968 2001: A Space Odyssey – imagining where AI could lead: Minsky influenced science fiction too. He advised Stanley Kubrick on the film 2001: A Space Odyssey, featuring an intelligent computer, HAL 9000. 1969 Tough problems to crack: AI was lagging far behind the lofty predictions made by advocates like Minsky – something made apparent by Shakey the Robot - the first general-purpose mobile robot able to make decisions about its own actions by reasoning about its surroundings But it was painfully slow, even in an area with few obstacles. Credits/References: BBC

1973 The AI winter: By the early 1970s AI was in trouble. Millions had been spent, with little to show for it. 1981 A solution for big business: The moment that historians pinpoint as the end of the AI winter was when AI's commercial value started to be realized, attracting new investment. The first successful commercial expert system, known as the RI, began operation at the Digital Equipment Corporation helping configure orders for new computer systems. By 1986 it was saving the company an estimated $40m a year. 1990 Back to nature for 'bottom-up' inspiration: Then AI scientist Rodney Brooks published a new paper: Elephants Don’t Play Chess. Brooks argued that the top-down approach of pre-programming a computer with the rules of intelligent behaviour was wrong. He helped drive a revival of the bottom-up approach to AI, including the long unfashionable field of neural networks. 1997 Man vs machine: Fight of the 20th Century: Supporters of top-down AI still had their champions: supercomputers like Deep Blue, which in 1997 took on world chess champion Garry Kasparov. The supercomputer won the contest, dubbed 'the brain's last stand', with such flair that Kasparov believed a human being had to be behind the controls. 2002 The first robot for the home: Rodney Brook's spin-off company, iRobot, created the first commercially successful robot for the home – an autonomous vacuum cleaner called Roomba. 2005 War machines: Having seen their dreams of AI in the Cold War come to nothing, the US military was now getting back on board with this new approach. They began to invest in autonomous robots. BigDog, made by Boston Dynamics, was one of the first. Credits/References: BBC

2008 Starting to crack the big problems: In November 2008, a small feature appeared on the new Apple iPhone – a Google app with speech recognition. It seemed simple. But this heralded a major breakthrough. At first it was still fairly inaccurate but, after years of learning and improvements, Google now claims it is 92% accurate. 2010 Dance bots: At the same time as massive mainframes were changing the way AI was done, new technology meant smaller computers could also pack a bigger punch. These new computers enabled humanoid robots, like the NAO robot, which could do things predecessors like Shakey had found almost impossible. At Shanghai's 2010 World Expo, some of the extraordinary capabilities of these robots went on display, as 20 of them danced in perfect harmony for eight minutes. 2011 Man vs machine: Fight of the 21st Century: In 2011, IBM's Watson took on the human brain on US quiz show Jeopardy. This was a far greater challenge for the machine than chess. Watson had to answer riddles and complex questions. Watson trounced its opposition – the two best performers of all time on the show. The victory went viral and was hailed as a triumph for AI. 2016 Are machines intelligent now? From Google's billion dollar investment in driverless cars, to Skype's launch of real-time voice translation, intelligent machines are now becoming an everyday reality that would change all of our lives. A short and exciting video about the history and evolution of AI Credits/References: BBC

Ai is booming (1/3) AI IS AT THE PEAK OF HYPE
AI features prominently on analyst firm Gartner's Hype Cycle for Emerging Technologies Unlikely that all of the technologies flagged up in Gartner's Hype Cycle will make it to the mainstream. But there's enough current interest and activity to ensure that some will -- and when they do, there will be serious implications for businesses. Credits/References: Gartner

Ai is booming (2/3) Resurgence Of Startup Funding Activity
The Machine Learning (Applications) category is leading with over $2B in funding and 263 companies. Natural Language Processing is the runner-up in both stats with $662M in funding and 154 companies. Credits/References: Venture Scanner

Ai is booming (3/3) Artificial Intelligence is the new Big Data
International Data Corp. predicts the worldwide market for cognitive software platforms and applications, which roughly defines the market for AI, to grow to $16.5 billion in 2019 from $1.6 billion in 2015 with a CAGR of 65.2%. The market includes offerings from both established tech giants and AI startups. CB Insights Trends is software that analyzes millions of media articles to programmatically identify and understand the rate of adoption of emerging technologies and innovations. Credits/References: CB Insights

Why is ai booming? (1/2) What is an Artificial Neural Network (ANN) ?
The current boom in AI is based on an old idea, with a modern twist: so-called artificial neural networks (ANNs), modeled on the architecture of the human brain. What is an Artificial Neural Network (ANN) ? Why ANNs weren’t as effective in the past? ANNs were starting to achieve some useful results in the early 1990s, for example in recognizing handwritten numbers. But attempts to get them to do more complex tasks ran into trouble; neural networks learn by example, and the standard training technique didn’t work with larger (or “deeper”) networks with more layers. After a flurry of excitement, enthusiasm for ANNs waned. Credits/References: Economist

Why is ai booming? (2/2) 3 Changes that have lead to the revival of Artificial Neural Networks (ANNs) and AI in the past few years 1 First, new training techniques made training deep networks feasible. 2 Second, the rise of the internet has made billions of documents, images and videos available for training purposes. But #2 requires a lot of number-crunching power and powerful hardware, which is where the third element comes in. 3 Around 2009, several AI research groups realized that graphical processing units (GPUs), the specialized chips used in PCs and video-game consoles to generate fancy graphics, were also well suited to modeling neural networks. GPUs could speed up training of its deep neural networks nearly a hundredfold, for example. = AI now powers everything from Google’s search engine, Facebook’s automatic photo tagging, Apple’s voice assistant, Amazon’s shopping recommendations to Tesla’s self-driving cars. “A CIO pursuing AI a decade ago would have spent more than 100% of his or her budget on compute power. Now it’s in the range of 10% to 20%, which frees up more resources for acquiring data sets and developing new algorithms.” – IBM Credits/References: Economist, WSJ

The current state of ai

Ai is everywhere “Deep learning helps Alphabet’s Google improve its search results” “Airbnb uses machine learning to recommend the best place to stay” “Big tech companies have developed AI-powered virtual assistants” “General Electric uses computer vision systems to quickly identify cracks in jet engine blades” “MasterCard uses machine learning tools to analyze more than 1.3 billion transactions per day and help detect fraud” “Nasdaq is using artificial intelligence systems that parse traders’ chatter to spot potential insider trading” Credits/References: WSJ

How are businesses adopting ai? (1/3)
80 per cent of the underlying data being processed in the financial services sector remains either semi-structured or not structured and has to be processed manually. With AI, firms can analyze and contextualize such data almost instantly. The AI-powered “financial services assistant” is capable of performing deep-content analysis and evidence-based reasoning to accelerate and improve decisions. For example, a bank could use the system to make better recommendations of financial products based on comprehensive analysis of market conditions, the client’s past decisions, recent life events and available offerings. AI has applications is in compliance, fraud detection and security. Integrating structured and unstructured data ensures compliance rules are being applied and can help to detect offences, such as money laundering and insider trading. Natural language processing systems can uncover subtle cues in transactions that might indicate behavior that does not show up in the numbers. So-called “know your customer” systems are another widespread use of AI to manage unstructured and constantly changing data in order to assess risk. AI IN FINANCE Credits/References: Raconteur

A proven application for AI in law is to screen claims for personal injury damages for signs of fraudulent behavior. AI is opening access to the law as systems become available that can reliably draft documents, such as tenancy and employment contracts, to suit individual circumstances. AI will make document assembly systems originally developed for lawyers accessible to lay users who need to draw up legal contracts on hand-held computers. AI IN LAW Intelligent advertising hoardings capable of sensing the presence of passers-by before displaying an advertisement and learning from their individual reactions how relevant it is. Application of pattern recognition and cognitive learning systems to help process sales leads e.g. “Rachel” a virtual persona equipped with such technology from supplier Conversica, is being used in vertical markets, including technology, automotive, education and financial services. AI could automate the process of sorting predictively scored companies and contact profiles into campaigns by buying stage, and the custom segment is automatically deployed for programmatic ad targeting. AI IN MARKETING & ADVERTISING Credits/References: Raconteur

Today: Farming decisions based on tradition and intuition Future: Machine learning algorithms using sensor data and aerial imaging help farmers make intelligent data based decisions increasing yield. AI IN AGRICULTURE Today: Cars driven by humans, prone to errors (nearly 1.3M people die in road accidents every year) Future: Driverless cars – leading to comfortable experience & less human fatalities. AI IN AUTOMOTIVE Today: Cumbersome & time wasting Voice Menu … Press 1 for Credit Cards, Press 2 for Debit Cards, Press 3 for Loans... Future: Virtual assistant that can converse like humans & assist with ease AI IN CUSTOMER CARE Today: Mundane tasks in office leading to decreased productivity Future: Automatic meeting schedulers, note takers, speech to text transcription & virtual personal assistants will improve productivity AI IN THE ENTERPRISE Credits/References: Raconteur, Ajit Nazre + Rahul Garg

What does the ai vendor landscape look like?
Credits/References: Shivon Zilis

Making sense of the ai vendor landscape (1/3)
SHIVON ZILIS’ FRAMEWORK FOR UNDERSTANDING THE AI LANDSCAPE (1/3) “Panopticons” Collect A Broad Dataset Machine intelligence starts with the data computers analyze, so the companies Zilis calls “panopticons” are assembling enormous, important new datasets. Defensible businesses tend to be global in nature. “Global” is very literal in the case of a company like Planet Labs, which has satellites physically orbiting the earth. Or it’s more metaphorical, in the case of a company like Premise, which is crowdsourcing data from many countries. With many of these new datasets we can automatically get answers to questions we have struggled to answer before. There are massive barriers to entry because it’s difficult to amass a global dataset of significance. Examples include Planet Labs, Premise and Diffbot. “Lasers” Collect A Focused Dataset The companies Zilis likes to call “lasers” are also building new datasets, but in niches, to solve industry-specific problems with laser-like focus. Successful companies in this space provide more than just the dataset — they also must own the algorithms and user interface. They focus on narrower initial uses and must provide more value than just data to win customers. The products immediately help users answer specific questions like, “how much should I water my crops?” or “which applicants are eligible for loans?” This category may spawn many, many companies — a hundred or more — because companies in it can produce business value right away. Examples include Tule Technologies, Enlitic, InVenture, Conservation Metrics, Red Bird, Mavrx and Watson Health. Credits/References: Shivon Zilis

SHIVON ZILIS’ FRAMEWORK FOR UNDERSTANDING THE AI LANDSCAPE (2/3) “Alchemists” Promise To Turn Your Data Into Gold These companies have a simple pitch: Let me work with your data, and I will return gold. Rather than creating their own datasets, they use novel algorithms to enrich and draw insights from their customers’ data. They come in three forms: Self-service API-based solutions, Service providers who work on top of their customers’ existing stacks, Full-stack solutions that deliver their own hardware-optimized stacks. Because the alchemists see across an array of data types, they’re likely to get early insight into powerful applications of machine intelligence. If they go directly to customers to solve problems in a hands-on way (i.e., with consulting services), they often become trusted partners. Examples include Nervana Systems, Context Relevant, IBM Watson, Metamind, AlchemyAPI (acquired by IBM Watson), Skymind, Lucid.ai and Citrine. “Gateways” Create New Use Cases From Specific Data Types These companies allow enterprises to unlock insights from a type of data they had trouble dealing with before (e.g., image, audio, video, genomic data). They don’t collect their own data, but rather work with client data and/or a third-party data provider. Unlike the Alchemists, who tend to do analysis across an array of data types and use cases, these are specialists. What’s most exciting here is that this is genuinely new intelligence. Enterprises have generally had this data, but they either weren’t storing it or didn’t have the ability to interpret it economically. All of that “lost” data can now be used. Examples include Clarifai, Gridspace, Orbital Insight, Descartes Labs, Deep Genomics and Atomwise. “Magic Wands” Seamlessly Fix A Workflow These are SaaS tools that make work more effective, not just by extracting insights from the data you provide but by seamlessly integrating those insights into your daily workflow, creating a level of machine intelligence assistance that feels like “magic.” They are similar to the Lasers in that they have an interface that helps the user solve a specific problem — but they tend to rely on a user’s or enterprise’s data rather than creating their own new dataset from scratch. For example, Textio is a text editor that recommends improvements to job descriptions as you type. With it, I can go from a 40th percentile job description to a 90th percentile one in just a few minutes, all thanks to a beautifully presented machine learning algorithm. Examples include Textio, RelateIQ (acquired by Salesforce), InboxVudu, Sigopt and The Grid Credits/References: Shivon Zilis

SHIVON ZILIS’ FRAMEWORK FOR UNDERSTANDING THE AI LANDSCAPE (3/3) “Navigators” Create Autonomous Systems For The Physical World Machine intelligence plays a huge role in enabling autonomous systems like self-driving cars, drones and robots to augment processes in warehouses, agriculture and elderly care. This category is a mix of early stage companies and large established companies like Google, Apple, Uber and Amazon. Such technologies give us the ability to rethink transportation and logistics entirely, especially in emerging market countries that lack robust physical infrastructure. We also can use them to complete tasks that were historically very dangerous for humans. Examples include Blue River Technologies, Airware, Clearpath Robotics, Kiva Systems (acquired by Amazon), 3DR, Skycatch, Cruise Automation and the self-driving car groups at Google, Uber, Apple and Tesla. “Agents” Create Cyborgs And Bots To Help With Virtual Tasks Sometimes the best way to use machine intelligence is to pair it with human intelligence. Cyborgs and bots are similar in that they help you complete tasks, but the difference is a cyborg appears as if it’s a human (it blends human and machine intelligence behind the scenes, has a proper name and attempts to interact like a person would), whereas a bot is explicitly non-human and relies on you to provide the human-level guidance to instruct it what to do. Cyborgs most often complete complex tasks, like customer service via real-time chat or meeting scheduling via (e.g., Clara from Clara Labs or Amy from x.ai). Bots tend to help you perform basic research, complete online transactions and help your team stay on top of tasks (e.g., Howdy, the project management bot). In both cases, this is the perfect blending of humans and machines: The computers take the transactional grunt work pieces of the task and interact with us for the higher-level decision-making and creativity. Examples include Clara, x.ai, Facebook M, Digital Genius, Kasisto and Howdy. A short video where Zilis explains the AI landscape

adopting ai in your business

Adopting ai in your business
TECHNICAL CONSIDERATIONS/CHALLENGES (1/2) Tech vendors have developed broad platforms aimed at bringing machine learning tools to corporate customers. Experts say business users will favor industry-specific applications. When you build a very narrowly focused artificial intelligence it helps the algorithms train faster and you end up with a system that’s more accurate. More specific applications also may make it easier for CIOs to create performance metrics around them. AI PLATFORM VS. SPECIFIC APP Deploying machine learning tools will force companies to think about new ways to collect and store data, develop applications, design algorithms and build user interfaces. AI projects will require input from many lines of business, which could make implementation challenging. DATA Credits/References: WSJ

Adopting ai in your businesS
TECHNICAL CONSIDERATIONS/CHALLENGES (2/2) General confusion about the capabilities of artificial intelligence technologies may slow adoption. GARTNER: “People are selling big dreams and visions of what this can do, and they’re having trouble landing this in a way that’s practical and realistic …Most IT organizations don’t have the skills to understand what can and can’t be done.” No AI system yet developed will magically fit every use case. If a vendor tells you that a model got state of the art results out of the box, they're either exceedingly lucky or giving you a fairy tale version of reality. HYPE Ask these questions to separate hype from reality "How much training data is required?" "Can this work unsupervised (= without labelling the examples)?" "Can the system predict out of vocabulary names?" (i.e. Imagine if I said "My friend Rudinyard was mean to me" - many AI systems would never be able to answer "Who was mean to me?" as Rudinyard is out of its vocabulary) "How much does the accuracy fall as the input story gets longer?" "How stable is the model's performance over time?" The real world rarely looks like a dataset. Very few datasets are entirely representative of what the task looks like in the real world. Any claim of advanced research without publication is suspect at best AI doesn't change the base use case or business fundamentals. If the business plan doesn't work with free humans, AI won't save it. DEALING WITH THE HYPE Credits/References: WSJ, Stephen Merity

“PEOPLE” CONSIDERATIONS/CHALLENGES JOBS Technology advances have always eliminated some jobs and created others, leaving social change -- in the form of displaced 'legacy' workers -- in their wake. There's no reason to suppose that this trend will not continue with AI. Deloitte: "We cannot forecast the jobs of the future, but we believe that jobs will continue to be created, enhanced and destroyed much as they have in the last 150 years." Forrester: "While automation will lead to a net loss of 9.1 million US jobs by 2025, that's nowhere near the 69 million that many pundits have predicted." Forrester also foresees a partnership rather than an adversarial relationship: "Advances in automation technologies will mean that humans increasingly work side by side with robots, software agents and other machines." DECISION MAKING: AI VS HUMANS As AI advances and more machines start making unsupervised decisions, companies will face tough questions about exactly when humans do or don’t need to be involved in decision making. “The consequences of mistakes at the moment are just unknown. The recent crash of an autopilot-equipped Tesla shows the challenges of coordinating the behavior of humans and machines. EFFECT ON THE WORKFORCE Businesses must grapple with the effect intelligent machines will have on their workforce. That not only means finding the skilled talent to work in tandem with smart machines, but also deciding how to structure their firms as more tasks are automated or require knowledge of data science. Credits/References: WSJ, ZDNet

BUILDING YOUR AI TEAM: CHALLENGES (1/3) Companies looking to start an AI team often get overwhelmed with the challenges and specific characteristics of hiring, building and growing a team. Confusion caused by all the terms, praises and buzzwords around certain technologies, algorithms and skills. Starting a team of this kind is not the same as it is with an average software development team. Profiles are more specific, terminology is more exotic, and there is little consensus on the market regarding best practices and the state of the art. Severe skill shortage. Experts claim that data scientists are unicorns in short supply. The talent crunch in machine intelligence will make it look like we had a glut of data scientists. CHALLENGES Credits/References: OREILLY, Venturebeat

BUILDING YOUR AI TEAM: CONSIDERATIONS (2/3) It should be very clear from the beginning for everyone exactly where in the organizational chart the team will be located and who the main stakeholders will be. Some organizations put the data science team under the CTO, others under the CFO or even the CMO, others prefer a federated system with specialists distributed across departments and supervised by a project manager, while others go for the R&D route where the team does not have a specific agenda or stakeholder and has an open hand to decide. The decision depends on the company organization, culture, and resources, as well as the team’s mission. The risk of not deciding this from the beginning can lead to confusion in the daily activities of the team. ACCOUNTABILITY Technical talent in this area does not come at a low price, so budgets have to be properly planned. For a company with 300 or more employees trying to create a centralized team with a specific mission (e.g. recommendation engines, customer reactivation, etc.) a good first start is a team of 5 to 8 people, where one is the technical project manager, 1-2 are the hardcore data scientists responsible for modeling, and 3-5 are the data engineers deploying the production code. Over time, teams can become larger and similar teams with different missions can surge. Therefore, a quality team represents a significant commitment and this should be clear for every stakeholder. RESOURCES Credits/References: Venturebeat

BUILDING YOUR AI TEAM: COMPOSITION (3/3) The person has 3 to 5 years experience managing similar teams dealing with quantitative subjects. Preferably, this person has a solid technical background and although she is not expected to code, she is capable of doing it. This person not only has the skills expected in a project manager, but has also an understanding of the algorithms and techniques used by the team and great if she can also do code reviews. TECHNICAL PROJECT MANAGER Someone with a solid quantitative background. Ideally, she holds a Ph.D in the fields of Physics, Mathematics, Computer Science, Biology, or associated disciplines. This person should be judged by the quality of her research, where she has published, and what she has contributed. It is entirely possible to be an expert in machine learning and be really bad in software development. Hence, it is very important to not assume anything and double-check her coding skills. Unless you want to develop a more academic R&D team, somebody who cannot code will not be very helpful, especially in the early days of the team. Additionally, it is important to verify how hands-on the individual is, as candidates from academia sometimes have wrong expectations of what industry needs from them. DATA SCIENTIST This person does not need to be very academic. She can be a solid software developer with an interest in quantitative topics. This person must have a very solid understanding of algorithms, data structures and software engineering in general. Double-check the algorithms part (especially computational complexity), as many engineers have a poor understanding of the subject, yet it is essential for every robust data team. Overall, her code must be excellent. Try to look for individuals who actively contribute to open source projects. Ideally, this person uses the same technology stack as your data scientists (e.g. Python, Scala, etc). DATA ENGINEER Credits/References: Venturebeat

The future of ai in business

The future of ai in business (1/3)
BOTH BUSINESSES AND AI TECH COMPANIES WILL MOVE AT A FAST PACE Today Struggle to make heads or tails of artificial intelligence services. Potential enterprise customers say: “these AI tech companies are trying to sell me snake oil” … “they can’t even explain to me what they do.” The corporates want to know what current business problems the technologies offered by the AI companies can solve, and don't care about the technology itself. The AI companies, on the other hand, just want to talk about their algorithms and how their platform could solve hundreds of problems. Future Heavy investment in becoming “artificial intelligence literate.” Execs change their organizations to make use of new AI technologies Many different roles across the organization care about AI (from CEOs to technical leads to product managers). AI companies figure out that they need to speak the language of solving a business problem. They start packaging solutions to specific business problems as separate products and branding them that way. They start working alongside the businesses to create a unique solution instead of just selling the technology itself, being one part educator and one part executor. BUSINESSES AI Technology Companies Credits/References: Shivon Zilis

HEALTHCARE (automated diagnostics, early disease detection based on genomics, algorithmic drug discovery) AGRICULTURE (sensor- and vision-based intelligence systems, autonomous farming vehicles) Transportation and logistics (self-driving cars, drone systems, sensor-based fleet management) Financial services (advanced credit decisioning) AI technologies will transform the business world by starting in regulatory grey areas Startups are making a global arbitrage (e.g., health care companies going to market in emerging markets, drone companies experimenting in the least regulated countries). The “fly under the radar” strategy. Some startups are being very careful to stay on the safest side of the grey area, keep a low profile, and avoid the regulatory discussion as long as possible. Big companies like Google, Apple, and IBM are seeking out these opportunities because they have the resources to be patient and are the most likely to be able to effect regulatory change — their ability to affect regulation is one of their advantages. Startups are considering beefing up funding earlier than they would have, to fight inevitable legal battles and face regulatory hurdles sooner. Strategies will evolve to overcome the difficulties of entering regulatory grey areas REGULATION WILL IMPACT WHICH AREAS WILL GET TRANSFORMED FIRST Credits/References: Shivon Zilis

THE FUTURE OF AI IS EXCITING! WHAT TO EXPECT IN THE NEAR FUTURE? The practical side of AI technologies will flourish. Most new entrants will avoid generic technology solutions, and instead have a specific business purpose to which to put artificial intelligence. More combinations of the practical and eccentric. A few years ago, a company like Orbital Insight would have seemed farfetched — wait, you’re going to use satellites and computer vision algorithms to tell me what the construction growth rate is in China!? — and now it feels familiar. Researchers will continue doing things that make us stop and say, “Wait, really?”. Like creating fairy godmother drones to help the elderly, computer vision that detects the subtle signs of PTSD, and autonomous surgical robots that remove cancerous lesions Overall, agents will become more eloquent, autonomous systems more pervasive, artificial intelligence more…intelligent. Expect more magic in the years to come! Credits/References: Shivon Zilis

THANKS

80 per cent of the underlying data being processed in the financial services sector remains either semi-structured or not structured and has to be processed manually. With AI, firms can analyze and contextualize such data almost instantly. The AI-powered “financial services assistant” is capable of performing deep-content analysis and evidence-based reasoning to accelerate and improve decisions. For example, a bank could use the system to make better recommendations of financial products based on comprehensive analysis of market conditions, the client’s past decisions, recent life events and available offerings. AI has applications is in compliance, fraud detection and security. Integrating structured and unstructured data ensures compliance rules are being applied and can help to detect offences, such as money laundering and insider trading. Natural language processing systems can uncover subtle cues in transactions that might indicate behavior that does not show up in the numbers. So-called “know your customer” systems are another widespread use of AI to manage unstructured and constantly changing data in order to assess risk. AI IN FINANCE Credits/References: Raconteur

A proven application for AI in law is to screen claims for personal injury damages for signs of fraudulent behavior. AI is opening access to the law as systems become available that can reliably draft documents, such as tenancy and employment contracts, to suit individual circumstances. AI will make document assembly systems originally developed for lawyers accessible to lay users who need to draw up legal contracts on hand-held computers. AI IN LAW Intelligent advertising hoardings capable of sensing the presence of passers-by before displaying an advertisement and learning from their individual reactions how relevant it is. Application of pattern recognition and cognitive learning systems to help process sales leads e.g. “Rachel” a virtual persona equipped with such technology from supplier Conversica, is being used in vertical markets, including technology, automotive, education and financial services. AI could automate the process of sorting predictively scored companies and contact profiles into campaigns by buying stage, and the custom segment is automatically deployed for programmatic ad targeting. AI IN MARKETING & ADVERTISING Credits/References: Raconteur

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Artificial intelligence as a business technology

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Presentation on theme: "Artificial intelligence as a business technology"— Presentation transcript:

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