These types of situations are what programs such as the MIT-developed Moral Machine have been looking to unlock. The site has for the past four years canvassed public opinion by presenting a series of ethical dilemmas: should an autonomous car protect its passenger for instance, or a pedestrian, in a particular scenario?

The wider point, however, is that such autonomy – or to put it more romantically, machines thinking for themselves – requires vast amounts of data. Data which, to date, has not been calibrated. Would you entrust your safety to your vehicle?

This is an analogy which Dorian Selz, co-founder and CEO of Squirro, uses to explain why the great AI revolution might take longer than many think. “I think we’re going to see massive advances in the underlying technology over the next 10 years – we are probably going to see some level of breakthrough to have some low level of intelligence in these machines,” Selz tells AI News. “I can imagine that – but it will take time until it is really adopted widely.”

For the CEO of an AI platform provider, this may be an interesting position to take – something which Selz accepts. Yet Squirro’s business case may give an indication. The company’s modus operandi is  taking organisations’ unstructured data sets – everything from emails, to market reports, to PowerPoint presentations and Word documents – and utilising what it calls ‘augmented intelligence’ to surface greater insights.

This collaborative process not only leans on the human factor, but takes time to appreciate. “Most companies have ratios of 90% to 99% of all data they generate or acquire never being used beyond the first use,” explains Salz. “That is the equivalent of buying a car, driving it from the dealership back to your garage, and then throwing away the key and never driving that car again.

“If you say data is the new oil, at the same time companies are not making use of that data,” he adds. “What works wonderfully well for numbers doesn’t work at all for text in an Excel.”

Selz knows of which he speaks with regard to how companies use and stockpile data. Squirro is the fourth company he has co-built, with a previous success being Swiss homegrown search engine local.ch. With Squirro, which promises benefits from customer and service insights to cognitive search, the goal is for this data to complement business processes and working methods.

Take an accounts department, for example, whereby the data can be able to show not just whether a client has paid or not, but any relevant news or market research which could impact their ability to pay. In the supply chain, issues can be identified several levels down the chain which may impact the direct supplier.

“If you look at any company today, small or big, their IT landscape is effectively a combination of boxed applicastions – a supply chain system at the back of the company, an ERP system in the middle, some level of CRM at the front and support systems in the background,” explains Selz. “The bigger the company, the more of that stuff you have, but it’s still essentially a landscape of boxed IT applications.

“The future we see is a future where you’re going to see a machine learning-driven layer that weaves all these different data silos together,” Selz adds. “We foresee a future where these types of intimately weaved informational fabrics become the new norm in any enterprise.

“It doesn’t replace you – I don’t believe in that at all. But it will support you in decision making.”

One customer which exemplifies this ‘weaving’ of data is Brookson, a UK-based local accountancy firm for contractors. The company had already been using Squirro to classify data coming in to expedite the administrative and call centre process, but the next step was particularly noteworthy: using the technology to assess future customer relationships. Based on a roadmap created from previous customers, good, bad or indifferent, every new interaction can be assessed and forecast using pre-defined journeys.

“I love to retell that story, because it shows that AI per se is not just for the big boys,” says Selz. “As a nimble, agile, SME enterprise, you can use this type of technology to really fundamentally put yourself in a better position in the marketplace.”

This is an interesting point; plenty of research, not least a study from VC company Work-Bench in 2018, has argued that bigger companies are at a natural advantage with AI because they can hoover up the best talent. But as AI expertise does not automatically equal big businesses, big effects are not the results of such technology yet, Selz argues – going back to his original theme around transformation.

“Everybody talks about these big intelligence machines that do these fantastic things for companies and automate,” he explains. “Transform entire industries? I don’t really believe that.

“If you look at many machine learning techniques in use today, from the more simplistic ones like SVM, Bayesian algorithms, all the way to more sophisticated deep learning models, at the end, they’re not really intelligent,” Selz adds. “It’s pattern matching at the power of whatever computer you have.”

Selz notes that ‘none of the lovely AI engines had foreseen the coronavirus’. Given the reactive, rather than proactive, steps taken by governments worldwide, it wasn’t just the machines who were flat-footed. Looking at various stories assessing AI’s impact on Covid-19, many argue its role is to assess what will happen next – rather, assessing the damage once the horse has bolted – with a Singapore research unit predicting last month, with extreme caution, that the pandemic will end globally by December.

This informs Selz’s overall theory that we are nowhere near ready for primetime in B2B just yet. “It’s outright dangerous to think such an AI engine can suddenly transform your business if it is so limited in its capabilities and capacity,” he says.

“A customer relationship is more than what you have in your data sets,” Selz adds. “Even in retail volume segments, no one has a fully digitised customer relationship yet. If you don’t have it fully digitised, every aspect of it, why would you entrust some anonymous algorithm call method to evaluate and effectively decide on the future of that relationship?

“No sane person would do that.”

Editor’s note: You can find out more about augmented intelligence at AIM, a virtual event hosted by Squirro on June 23-25.

 Attend the co-located AI & Big Data Expo events with upcoming shows in Silicon Valley, London, and Amsterdam to learn more. Co-located with the IoT Tech Expo, , and Cyber Security & .

The post Dorian Selz, CEO, Squirro: Why the AI revolution will take time appeared first on AI News.

The study argued that the largest technology companies were winning at artificial intelligence (AI) – not surprising, given they can hoover up all the best talent – but more importantly, business intelligence (BI) vendors wanting to ensure their future going forward needed to beef up their skillsets with AI. “Expect all modern BI vendors to release an AutoML product or buy a startup by [the] end of next year,” the report noted.

While that may not be entirely true, one company which is certainly ahead of the curve in this instance is data analytics platform provider Qlik. The company is focusing on what it calls ‘augmented intelligence’ – of which more shortly – with Qlik Insight Bot focusing on AI-powered conversational analytics.

At the forefront of this is Elif Tutuk (above), Qlik’s head of research. Ahead of her appearance at AI & Big Data Expo in Silicon Valley in November, AI News caught up with Tutuk to discuss Qlik’s research initiatives and concerns around artificial intelligence among others.

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AI News: Hi Elif. What research initiatives are you and your team currently working on and how is this benefiting both Qlik and the wider industry at large?

Elif Tutuk: The Qlik Research team currently focuses on ‘augmented intelligence.’ Augmented intelligence is an approach that brings together the best of machine intelligence and human intuition to speed up time-to-insight, surface new and unexpected discoveries, and drive data literacy for users in any role and at any skill level.

These innovations lead new technologies that create a multiplier effect, where the human-machine collaboration outpaces anything either the human or the machine could do on its own. They help to decrease bias in human decisions and ultimately provide more adoption of analytics and the insights they provide.

AI: You wrote a piece last month exploring the issues with regards to bias in AI. What other concerns do you see organisations facing and encountering when they try to introduce AI/ML/automation into their workflows?

ET: There are niche applications for artificial intelligence that rely completely on machine automation, but more complex business problems require interaction and human capabilities. At Qlik, we do not believe that the ultimate purpose of the AI is to replace the user-driven analysis with a black box approach. Instead we argue that machine intelligence and automation must be combined with human-centred analysis. In this way, all users, even if with different roles and abilities, have access to data literacy.

A new school and culture is therefore needed to increase data literacy. Data literacy is the ability to read, work, analyse and discuss with data. It enables people to correctly interpret analytics results and insights generated by the machine.

AI: Which areas do businesses need to concentrate on first with AI/ML initiatives? Are there any quick wins available short-term?

ET: One of the biggest challenges that companies are facing in this fourth industrial revolution, as I mentioned before, is the lack of data-literate talent. The Data Literacy Index compiled by Qlik shows how the leaders of large global companies almost unanimously believe that it is important to have data-literate employees – this is perhaps not surprising, given that having data literate employees increases corporate value by 3%-5%. Two thirds plan to implement data literacy in the company.

AI: One of your speaking sessions at AI & Big Data Expo is around the third generation of business intelligence (BI) and how BI is evolving with AI. Aside from big data, what other technologies do you see converging with AI and why?

ET: AI will push forward the ideas of transparency, of seamless interaction with devices and information, making everything personalised and easy to use. We will be able to harness that sensor data and put it into an actionable form, at the moment when we need to make a decision.

AI: Are BI vendors more generally at serious risk of being left behind if they don’t embrace AI technologies quickly?

ET: Augmented intelligence and applications of AI drive less-biased decisions and more impartial contextual awareness, transforming how users interface with data, make decisions, and act on insights. It expands who has access to insights from analytics by delivering analytics anywhere and to everyone in the organisation, and does so with less time, skill and interpretation bias than current manual approaches. The goal of BI is to enable fact-based decisions – and the application of AI is a key enabler for that.

AI: What message are you looking to get across to the audience from your sessions? What are you hoping they will take away from your talk?

ET: During my session, I will talk about how artificial intelligence is not purely ‘artificial’, and about the ideal approach amplifying human intuition with machine intelligence to create a powerful multiplier effect. I will use analogies between how the brain processes information and data analytics and talk about the need for new data structure approaches to decrease bias in AI. The audience will also learn about what to consider when evaluating AI in analytics.

The post Elif Tutuk, Qlik: On securing data literacy and the evolution of AI with BI appeared first on AI News.

First, let me try to define my terms, because data science is a slippery term that sloppy thinkers use interchangeably with other fashionable phrases. For me, it’s to be located at the intersection of mathematics, statistics, business domain knowledge, programming and research. It’s an attempt to combine the power of the technologist with the experience of the line-of-business person via the ability to mine data and apply statistical discipline and software engineering. Combine all this, and you have a chance to detect underlying patterns and see tease out meanings…you can see why these people are relatively scarce in number.

But if we accept the conventional wisdom that use of data can be a hugely powerful weapon – and we should – what can we do to bridge the gap between demand and supply of people with this cocktail of skills? The answer lies in reskilling and a programme of education and recruitment to create a new generation of data scientists from the ranks of developers, analysts, engineers and business users. Just as at times of national crisis the government could call on conscription, companies are recruiting armies of citizen data scientists.

Of course, they don’t have all the know-how listed in my attempt to define data science, but citizen data scientists are emerging as a pragmatic response to the need for better use of data.  These people often are employees who may not have been formally trained in mathematics or statistical analysis, but they are smart, good at communicating and possess a strong understanding of business challenges and needs. They should also be excited by the possibility of changing their organisations and learning to become better data scientists as they go along.

At its most sophisticated, data science can be a daunting task that involves complex algorithms and programming knowledge, but advances in AI, predictive analytics and cloud computing are democratising the discipline. In the age of Big Data and cloud platforms such as AWS and Microsoft Azure, every organisation has access to huge data sets and the compute capacity to query them. And the citizen data scientist who has deep knowledge of the company’s culture, market and profitability drivers might be in a better position than the specialist data analyst with only very limited knowledge of the employer’s gears and levers.

Paving the way

But how best to encourage and assist the citizen data scientist? It’s certainly the case that companies with an open culture of information sharing will be best positioned. They should also be equipped with analysis tools that mask underlying complexity and have user-friendly front-ends with wizards and templates. Thankfully, the rise of assistants such as Alexa and Siri are driving more conversational user interfaces that hide complex calculations and encourage users to ask direct business and operational questions. Similarly, software development is increasingly dependent on ‘low code’ platforms and visual drag-and-drop user interfaces. The user should have knowledge of data management and data structures and dependencies, but not necessarily programming.

This means that the citizen data scientists don’t need to call on the IT department at every turn, but can quickly create, test and deploy applications. And this in turn means that analyses can quickly be delivered to business decision makers and feedback accommodated. This again fits in with the zeitgeist of our times when rapid application development, agile and scrum methodologies are increasingly favoured over laborious, time-consuming waterfall approaches.

As organisations we all need to do better with data, corralling it, probing it, testing suppositions and seeking the insights that provide scope for competitive differentiation. Citizen data scientists provide a way to span the chasm between what we want to do and the shortfall in supply that lets us do it. If you feel that you could benefit from better use of the data, but struggle to hire experts, consider creating an internal programme and join the clarion call for citizen data scientists.

The post We need to do better with our data – can citizen scientists make a difference? appeared first on AI News.

Thankfully Jonathan Bowl, vice president and general manager of big data analytics and IoT at Hitachi Vantara, has a much more refreshing take on this aphorism. Data is not the new oil, he explains. Data does not run out; it does not deplete; and you can use it an infinite number of times. What’s more, the more you use it, the better it gets.

Bowl was speaking at the recent AI & Big Data Expo event in Amsterdam (below) around the ‘big data challenge’. The ultimate goal is not to go the same way as Kodak and Blockbuster. Yet in both cases the shoots of success were there, if only they were actioned upon. Kodak sold its digital imaging patents to a big tech consortium – Apple, Google, Facebook and more – after it had filed for chapter 11 bankruptcy in 2012, while the data Blockbuster held on its patrons had far more potential than realised.

Alongside those who fell by the wayside, Bowl noted those who were doing it right. Domino’s Pizza is a clear example of a company which significantly changed its fortunes. The firm realised its mission wasn’t to make pizzas, but deliver them, thereby thrusting it into the technology business. As per HBR’s 2016 analysis, fully half of the company’s employees at its HQ work in data and analytics.

It all fits in with Microsoft CEO Satya Nadella’s vision that every company needs to become a technology company. “Just get a taxi in London – you see all the apps that are appearing,” explains Bowl. “Organisations have always had the data – it’s less about collecting the data now, it’s using what you’ve already got.”

The pace of business is changing with it for those in the industry too. Cloudera and Hortonworks butted heads for years but came together in a $5.2 billion merger at the end of last year. Salesforce shelled out $15.7 billion – its biggest ever acquisition – for Tableau last month.

Bowl notes that Hitachi has had to make its own big moves – the acquisition of business intelligence (BI) software provider Pentaho in 2015 being one such example. “Hitachi is a good example of a company that recognised it needs to change,” he says. “Hitachi Ltd sells ‘trains as a service’. If you look at some of the solutions we’re trying to build, the applications we’re trying to build… Hitachi Vantara, which is just one company of Hitachi, its heritage is in infrastructure and storage solutions. If I look at how much it’s changed in the three years that I’ve been there, it’s almost night and day.”

It is never the easiest of journeys, however. Last month, Bowl wrote a blog which argued that only 5% of corporate data had been successfully analysed to drive business value. The rest is left uncovered in unconnected single devices or trapped in organisational silos. “Businesses are swimming in data but it’s only when you have it all under management that you can start to get a return on the data,” Bowl wrote.

One way of attempting to right this course is through hiring data scientists, but again you have to know what to do. Jonny Brooks-Bartlett, data scientist at Deliveroo, wrote about the travails of being hired by certain companies last year. The data scientist is employed with the hope of writing smart machine learning algorithms, but there is a ton of admin to plough through first; while the company simply wants ‘a chart that they could present in their board meeting each day.’

Hitachi employs many data scientists across its businesses, and Bowl’s team has a number of PhD data scientists where this topic crops up ‘a lot’, he explains. “It’s almost ticking a box – we’ve got some data scientists, we’re well on our way to transform. Ultimately, you’ve got to put them to work; you’ve got to empower them and make their job easier so they can spend the majority of their time doing the high-level work you hired them for in the first place – actually deriving value from data,” he explains.

“They can only work with the tools that they’ve got – if they can’t get to the data, if they can’t find the data, then that’s a challenge. It’s the old question – I don’t know what I don’t know. Until I get that, I can’t prove that one way or the other.

The answer, Hitachi Vantara argues, is DataOps. It’s a relatively new methodology, but it should really be considered standard business practice. It automates much of those processes taking up your data scientists’ time, ensuring your data is in the right place, at the right time and accessible to the right people. It’s a more agile, more deliberate approach to data science that ultimately enables better insights and better business decisions as a result.

“I can see that a being a real focus now, making sure you can unlock all the data in an enterprise,” Bowl adds. “There was this notion once of data lakes – but data’s got too many characteristics, storage requirements, performance characteristics. You’ve got to put data where it needs to go, on the platforms where it’s best to reside.”

 Attend the co-located AI & Big Data Expo events with upcoming shows in Silicon Valley, London, and Amsterdam to learn more. Co-located with the IoT Tech Expo, , and Cyber Security & .

The post Jonathan Bowl, Hitachi Vantara: On the changing face – and pace – of big data appeared first on AI News.

The latter is not overly surprising given Dias (left) describes himself as a ‘computational mathematician’ and that his undergraduate studies were in mathematics and astronomy. Yet the evident mix between maths and coding cannot be overemphasised. “If you don’t know the underlying mathematics that underpins an algorithm, you could apply it to the wrong data and if you make business decisions on that, you could kill the business,” he tells AI News.

“If you apply an algorithm to data, it’ll give you an answer,” he adds, laughing. “I’m always looking for people who have a solid maths background who can program – they don’t have to be software engineers – and they want to learn. I can teach them to talk to the business, I can teach them everything else, but if they don’t have those three it’s very difficult.”

Dias has been busy at Royal Mail since joining in 2017, building up a team of 25 data scientists and analysts. It was an opportunity he had been looking for, and Royal Mail gave him it on the condition of it being a blank canvas to work with.

“They had set everything up nicely – there was a platform we could work on,” he says. “It wasn’t perfect but they had done a lot of the groundwork, and they said ‘you’re the expert coming in, you tell us what you need and we’ll give it to you.’ They stayed true to their word, which was brilliant.”

The team works on initiatives split into three broad areas; revenue protection and recovery, customer experience, and operations. The latter is of particular interest because it covers both vehicles – optimising schedules, maintenance, streamlining deliveries – and personnel. The algorithms for internal use evidently make sense. These include identifying insights such as correlating a link between doing too much overtime and becoming ill. So for example, when an employee passes a certain threshold on overtime hours, the model could flag this with their manager to ensure their health and safety is prioritised when considering any further overtime work for them.

It’s important here to determine between the B2B side and the end customer who receives their post, thanks to the data science team, in a more seamless fashion. “For the business customer it will be recommended systems, customer segmentation, churn models, that kind of thing,” says Dias. “For the end customer it will be things like the estimated delivery window for parcels. We’ve built an algorithm for that.”

In terms of the sheer amount of data available, then the numbers are unsurprisingly vast; billions of parcels and letters to more than 29 million addresses, collecting from more than 100,000 post boxes and 10,000 post offices. The overall amount encompasses B2B customers, marketing and operations data, as well as Internet of Things (IoT) data coming from tracking devices. “It’s a lot of data, covering a lot of asepcts of the company, and it’s all available to us which is great,” says Dias. “It’s not always in one place and not always instantly available, but all we have to do is ask.”

The result is what Dias describes as ‘about £50 million of opportunities created’ in just the first year of building out the team, with many initiatives now moving out of the pilot stage. This journey of ‘from zero to data science’ is going to be explored further when Dias takes to the stage at AI & Big Data Expo Global on April 26 in London.

“A lot of people are wondering how to set up a data science function or team and, because I’ve managed to do it in two years, delivered stuff while building the team out, I thought it’d be useful to share what I did and the things that worked, and the things that didn’t work, to help others get there as well,” he says.

“It’s important for us to deliver fast because the hype cycle is coming to an end – and when people hire they want impact immediately.”

You can find out more about the session here.

/">AI & Big Data Expo events with upcoming shows in Silicon Valley, London, and Amsterdam to learn more. Co-located with the IoT Tech Expo, , & .

The post Ben Dias, Royal Mail: On building out data science teams and ensuring deep mathematical understanding appeared first on AI News.

Extracting value from sentiment analysis

Determining the value of cryptocurrencies is tricky. Unlike the conventional stock market, worth does not tightly correspond with factors such as cash flow or available assets. Instead, investors must rely on sentiment. But how do they make sense of all that is being said in a timely manner? Developer Teju Tadi believes he may have the answer.

The majority of cryptocurrency price movements can be determined by the herd instinct – when people think and act in the same way as the majority around them. Based on this, Teju says the sentiment analysis of news headlines, Reddit posts, and tweets is a good indicator of the direction of cryptocurrency price movements. Using recursive neural tensor networks (RNTNs), Teju is already analysing the sentiment of these short texts to develop an AI cryptocurrency trading bot.

Recursive neural tensor networks

RNTNs assess the semantic compositionality of text, which is vital to being able to accurately determine sentiment from a sparse set of information like a tweet.

RNTNs parse data into a binary tree. Specific vector representations are formed of all the words and are represented as leaves. From the bottom up, these vectors become the parameters to optimize and serve as feature inputs to a softmax classifier*. Vectors are classified into five classes and assigned a score.

“When similarities are encoded between two words, the two vectors move across to the next root. A score and class are outputted. A score represents the positivity or negativity of a parse while the class encodes the structure in current parses. The first leaf group receives the parse and then the second leaf receives the next word. The score of the parse with all three words are outputted and it moves on to the next root group,” says Teju.

‘The recursion process continues until all inputs are used up, with every single word included. In practical applications RNTN’s end up being more complex than this. Rather than using the immediate next word in a sentence for the next leaf group, an RNTN would try all the next words and eventually checks vectors that represent entire sub-parses. Performing this at every step of the recursive process, the RNTN can analyse every possible score of the syntactic parse.”

The figure below shows how a sentence is parsed and analysed using an RNTN approach. Teju also explains the process in this video.

Figure 1: Example of scoring from the Stanford Treebank.

Supporting technologies

As a member of the Intel® AI Academy, Teju was able to use the Intel® AI DevCloud to run the recurrent neural networks and experiment with Twitter data to see how the models were working. Running on Intel® Xeon® Scalable processors, the Intel AI DevCloud is preloaded with frameworks and tools to quickly launch machine learning and deep learning projects. These include neon™ framework, Intel® Optimization for Theano*, Intel® Optimization for TensorFlow*, Intel® Optimization for Caffe*, Intel® Distribution for Python* (including NumPy, SciPy, and scikit-learn*) and the Keras* library.

If you’re considering starting a deep learning project, reading To Get Started with Intel AI DevCloud will give you a good understanding of available models and how to initiate training using the AI DevCloud.

Opportunities for developers

Teju has set up a business Mycointrac*, focused on providing cryptocurrency market intelligence. “Once the product is fully developed,” he said, “I plan to utilise the data provided by it as one of the factors to make key investment decisions for my new cryptocurrency hedge fund, Sentience Investments L.P., which has been operational since January first. The plan is to develop trading strategies based on a number of high-frequency, machine-learning techniques, as well as deep learning and sentiment analysis.”

Teju hopes RNTNs will also help him take advantage of other opportunities, such as arbitrage – the simultaneous buying and selling of an asset in different markets. The profit being the difference between the two market prices.

The financial sector offers many great opportunities for developers who can utilise AI to address some of its most pressing challenges. For example, consistently gaining high returns on stock market investments.

The post How AI is helping to predict cryptocurrency value appeared first on AI News.

But what best practices can be transferred between the two, and what happens from here? Kimberly Nevala (left) is director of business strategies at analytics, business intelligence and data management provider SAS. AI News sat down with Nevala prior to her appearance at the AI & Big Data Expo in San Francisco later this month to get the lowdown on what SAS is doing and will be doing in this space, and the marriage between big data and AI.

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AI News: Could you define what your job role at SAS details and your day to day routine?

Kimberly Nevala: As a strategic advisor at SAS, my role encompasses market and industry research, content development, and advising our customers and prospects. In a nutshell, I help organisations understand the business potential and practical realities of emerging technologies such as AI and ML.

What are some of the initiatives you are working on at SAS with regards to AI and machine learning – and how are they impacting on customers?

We are very focused on not only the continuing evolution of AI capabilities but how AI/ML intersects with other domains to solve problems. This includes improving the IoT’s IQ through analytics. Some of our more recent work has included:

Making the IoT smart by marrying sensors and devices with streaming analytics. For example, Volvo and Mack Trucks are using sensor data from their on-road fleet to provide proactive real-time diagnostics 75% faster, resulting in less downtime and fewer critical breakdowns. The systems allow for rapid diagnostics and interventions from directing a driver to a local repair shop to uploading software updates while the truck is on the road.  Similarly, Konica Minolta Japan sped up its PDCA (plan-do-check-act) cycle by deploying multiple analytical models incorporating sensor and traditional data sources to improve areas as diverse as malfunction forecasts and management optimisation.

The modern CAO needs to be fluent in decision science, not just data science

In the more traditional customer engagement space, Daiwa Securities was able to deliver a 2.7x increase in customer purchase rates and a 50% reduction in customer departure rates with their new CRM system by incorporating AI/ML.

We are also passionate about the use of Data (and AI) for Good. A great example is our work with WildTracks to create the Footprint Identification Tool (FIT). FIT enables the non-invasive monitoring of endangered animals like Cheetahs through digital analysis of their footprints.

How has the technology landscape altered over the past 10 years in your opinion in terms of the rise of artificial intelligence and big data – and the increasing combination of the two?

The intersection of AI and big data provides the ability to deliver more targeted, timely, relevant insight in a pervasive and intuitive manner (“Alexa, …”). Delivering that simplicity requires an analytics and data ecosystem that is markedly more complicated than 10 years ago.

To that end, effectively deploying analytics from BI to AI is now an exercise in portfolio management. Complete with discrete customer segments (executives to data scientists), diverse data environments (warehouses to lakes), development methods (deterministic report development to dynamic probabilistic algorithmic modeling) and a wide spectrum of deployment options (supercharged dashboards to streaming event detection and response).

How does this all relate to more traditional business intelligence therefore?

To be clear: traditional BI isn’t gone – it has just gotten smarter and easier to access. It is also a requisite part of a robust analytics portfolio.  In fact, good discipline around BI (meaningfully measuring outcomes) is a key success factor for ensuring advanced AI/ML solutions deliver their intended value.

One of the eBooks you have written for SAS for ‘Portrait of a CAO’ back in 2014. What has changed between then and now? What difficulties would organisations looking to hire a chief analytics officer be facing today?

Analytics is increasingly integral to our business processes, products and services. Therefore, every executive today needs to be part CAO. That said, the complexity in the formal CAO role comes from the need to manage both technological and organisational change. The modern CAO needs to be fluent in decision science, not just data science. They also need to be collaborative – able to dynamically bring together collectives of internal and external experts to solve diverse problems.  All while spearheading an elite analytics vanguard: continuously researching emerging solutions and enabling the organisation to decide if, when and how to realise value.

What is the most exciting use case you have seen focused around AI or ML?

The environment is so dynamic right now, it’s hard to choose. I’m most inspired by the work we are doing in the health care and public service domains. For example, projects like Healthy Nevada Project  which aims to not just treat disease but promote health. This project is analysing genetic, clinical, environmental and socioeconomic data to better understand the complex interplay between these factors and health. There is also great work happening around rethinking how we serve and protect vulnerable populations.

Other use cases I find intriguing are from companies like Phylagen. Phylagen uses AI to analyse the microorganisms present on all things to identify where goods, materials and even people originated. This is a very different way of looking at the problem of supply chain integrity and even illicit trafficking. One that (from my perspective) doesn’t necessarily require the participation or the permission of the makers.

Effectively deploying analytics from BI to AI is now an exercise in portfolio management

I find the ability for AI/ML to enable a completely new approach to addressing a very old, complex problem striking.

What can we expect from SAS in the coming 12-18 months in this space?

Over the next 12-18 months we will continue to add additional analytics methods to the portfolio – with an emphasis on ML, DL, NLI and edge analytics. This includes features that enhance model interpretability and transparency. Also on the horizon: increasing usability and collaboration for all user types, continued enhancement of the integrated environment which supports the lifecycle from model development to deployment at scale (whether you code in SAS, Python, R, etc.) and embedding AI (including natural language interfaces) into our own solutions.

Finally, without giving too much away – what will the theme of your discussion be at AI & Big Data Expo later this month?

There are incredible synergies between the capabilities AI provides and the aspirations of digital transformation. AI also magnifies the challenges facing companies looking to “become digital”. This discussion will arm participants with the knowledge to successfully navigate these intersections.

Kimberly Nevala is speaking at the AI & Big Data Expo North America on November 28-29 at the Santa Clara Convention Center. Find out more about registering for the event by visiting here.

The post Kimberly Nevala, SAS: Bolstering BI with AI – and how every executive needs to be part chief analytics officer appeared first on AI News.

People.ai is an artificial intelligence (AI) powered sales management platform that helps sales managers make better and faster decisions by providing refreshing insights and utilising a data driven approach to management which involves making people decisions around hiring, coaching and managing sales reps in the right direction. It helps leaders make their people decisions based on data, not intuition. It has signed 50+ customers including Salesloft, Gainsight, and MemSQL, since graduating at the top of Y Combinator’s summer 2016 batch. It is instrumental in generating revenue for customers by increasing overall productivity providing clear, actionable, insights for closing more deals.

Oleg Rogynskyy, founder and CEO of People.ai, said: “CEOs have told us they’re constantly looking for new ways to drive more revenue from their sales teams. People.ai solves that exact problem. We’re giving sales leaders a repeatable formula they can use to win more deals and build a world class sales team.”

Nick Mehta, CEO at Gainsight, said: “People.ai has given us the ability to gather valuable insights into our sales cycle. We took the insights gathered in Q4/2016, applied them to Q1/2017 and were able to exceed our revenue targets. People.ai is changing how we hire, ramp, coach and manage our sales teams.”

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Unanimous AI, founded in 2015, offers what it calls ‘swarm AI’ technology which “amplifies human intelligence, empowering groups to harness their collective knowledge, wisdom and intuition by forming real-time AI systems,” in the company’s own words.

In other words, Unanimous AI aims to build a ‘super expert’ harnessing human capabilities with machine data usage. Partnering with TwinSpires, a horse racing betting firm, the aim was for not just a combination of individual race picks from the experts, but building an super expert and having them ‘think together’ as a real-time swarm moderated by AI algorithms.

Last year, the company made headlines by correctly predicting the superfecta – the top four horses in exact order – at odds of 542 to 1. This time round, the company’s picks were Classic Empire, which finished 4th, McCraken (8th), and Irish War Cry (10th). Always Dreaming (above) was ranked fourth by its system, while Lookin at Lee, a 32-1 outsider, confounded the experts by finishing second – with the odds of the superfecta as a result being a whopping 76,000 to 1.

Prior to this year’s race, the company, led by Louis Rosenberg, was confident yet cautious about its chances. “While predicting sports always involve a large element of chance, Unanimous AI taps the intelligence of groups and evokes the best possible prediction based on the available information,” he said. “We have seen this work in a wide range of fields, from forecasting movie box office to predicting the price of Bitcoin. We are excited to see how these handicappers do against one of the most unpredictable of events.”

This time around, the post-mortem was more circumspect, although the company noted the odds were more significantly against them this time around, as well as adding it had still placed more horses than the average individual expert.

“The swarming process amplified the intelligence of the experts, boosting the average performance from 1.6 horses correct up to 2.0 horses correct. That means the experts would have been better off, as a group, going with the swarm than going with their own individual picks,” the company noted. “But without 32-1 Lookin at Lee in anyone’s forecast, the players’ pool missed out on the massive superfecta.”

You can read the full post from Unanimous AI here.

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