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Category Archives: Machine Learning

Raleys Drive To Be Different Gets an Assist From Machine Learning – Winsight Grocery Business

Raleys has brought artificial intelligence to pricing not to necessarily to go toe-to-toe with competitors, but to differentiate from them, President and CEO Keith Knopf said.

Speaking in a presentation at the National Retail Federation show in New York, Knopf described how the West Sacramento, Calif.-based food retailer is using machine learning algorithms from partner Eversight to help manage its price perception amid larger, and often cheaper, competitorswhile optimizing revenue by driving unit share growth and margin dollars. That benefit is going toward what he described as a differentiated positioning behind health and wellness.

This is not just about pricing for the sake of pricing. This is pricing within a business strategy to differentiateand afford the investment in price in a way that is both financially sustainable and also relevant to the customer, Knopf said.

Raleyshas been working with Eversight for about four years, and has since invested in the Palo Alto, Calif.-based provider of AI-led pricing and promotion management. Knopf described using insights and recommendations derived from Eversights data crunching to support its merchants, helping to strategically manage the Rubiks Cube of pricing and promoting 40,000 items, each with varying elasticity, in stores with differing customer bases, price zones and competitive characteristics.

Raleys, Knopf said, is high-priced relative to its competitors, a reflection of its sizeand its ambitions. Were a $3 billion to $4 billion retailer competing against companies much larger than us, with much greater purchasing power and so for us, [AI pricing] is about optimization within our brand framework. We aspire to be a differentiated operator with a differentiated customer experience and a differentiated product assortment, which is guided more toward health and wellness. We have strong position in fresh that is evolving through innovation. But we also understand that we are a high-priced, high-cost retailer.

David Moran, Eversights co-founder, was careful to put his companys influence in perspective. Algorithms don't replace merchants or set a strategy, he said, but can support them by bringing new computing power that exceeds the work a merchant could do alone and has allowed for experimentation with pricing strategies across categories.In an example he shared, a mix of price changessome going up, others downhelped to drive overall unit growth and profits in the olive oil category.

The merchants still own the art: They are still the connection between the brand positioning, the price value perception, and they also own the execution, Knopf said. This technology gets us down that road much faster and with greater confidence.

Knopf said he believes that pricing science, in combination with customer relationship management, will eventually trigger big changes in the nature of promotional spending by vendors, with a shift toward so-called below the line programs, such as everyday pricing and personalized pricing, and less above the line mass promotions, which he believes are ultimately ineffective at driving long-term growth.

Every time we promote above the line, and everybody sees what everybody else does, no more units are sold in totality in the marketplace, it's just a matter of whos going to sell this week at what price, Knopf said.I believe that its in in the manufacturers best interest, and the retailers best interest, to make pricing personalized and relevant, and the dollars that are available today will shift from promotions into a more personalized, one-on-one, curated relationship that a vendor, the retailer and the customer will share.

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Being human in the age of Artificial Intelligence – Deccan Herald

After a while, everything is overhyped and underwhelming. Even Artificial Intelligence has not been able to escape the inevitable reduction that follows such excessive hype. AI is everything and everywhere now and most of us wont even blink if we are toldAI is poweringsomeonestoothbrush. (It probably is).

The phrase is undoubtedly being misused but is the technology too? One thing is certain, whether we like it or not, whether we understand it or not, for good or bad, AI is playing a huge part in our everyday life today huger than we imagine. AI is being employed in health, wellness and warfare; it is scrutinizing you, helping you take better photos, making music, books and even love. (No, really. The first fully robotic sex doll is being created even as you are reading this.)

However, there is a sore lack of understanding of what AI really is, how it is shaping our future and why it is likely to alter our very psyche sooner or later. There is misinformation galore, of course. Either media coverage of AI is exaggerated (as if androids will take over the world tomorrow) or too specific and technical, creating further confusion and fuelling sci-fi-inspired imaginations of computers smarter than human beings.

So what is AI? No, we are not talking dictionary definitions here those you can Google yourself. Neither are we promising to explain everything that will need a book. We are onlyhoping to give you aglimpse into theextraordinary promise and peril of this single transformative technology as Prof Stuart Russell, one of the worlds pre-eminent AI experts, puts it.

Prof Russell has spent decades on AI research and is the author of Artificial Intelligence: A Modern Approach, which is used as a textbook on AI in over 1,400 universities around the world.

Machine learning first

Otherexperts believe our understanding of artificial intelligence should begin with comprehending machine learning, the so-called sub-field of AI butone that actually encompasses pretty much everything that is happening in AI at present.

In its very simplest definition, machine learning is enabling machines to learn on their own. The advantages of thisare easy to see. After a while, you need not tell it what to do it is your workhorse. All you need is to provide it data and it will keep coming up with smarter ways of digesting that data, spotting patterns, creating opportunities in short doing your work better than you perhaps ever could. This is the point where you need to scratch the surface. Scratch and you will stare into a dissolving ethical conundrum about what machines might end up learning. Because, remember they do not (cannot) explain their thinking process. Not yet, at least. Precisely why, the professor has a cautionary take.

The concept of intelligence is central to who we are. After more than 2,000 years of self-examination, we have arrived at a characterization of intelligence that can be boiled down to this: Humans are intelligent to the extent that our actions can be expected to achieve our objectives. Intelligence in machines has been defined in the same way: Machines are intelligent to the extent that their actions can be expected to achieve their objectives.

Whose objectives?

The problem,writes the professor, is in this very definition of machine intelligence. We say that machines are intelligent to the extent that their actions can be expected to achieve their objectives, but we have no reliable way to make sure that their objectives are the same as our objectives. He believes what we should have done all along is to tweak this definition to: Machines are beneficial to the extent that their actions can be expected to achieve our objectives.

The difficulty here is of course that our objectives are in us all eight billion of us and not in the machines. Machines will be uncertain about our objectives; after all we are uncertain about them ourselves but this is a good thing; this is a feature, not a bug. Uncertainty about objectives implies that machines will necessarily defer to humans they will ask permission, they will accept correction and they will allow themselves to be switched off.

Spilling out of the lab

This might mean a complete rethinking and rebuilding of the AI superstructure. Perhaps something that indeed is inevitable if we do not want this big event in human history to be the last, says the prof wryly. As Kai-Fu Lee, another AI researcher, said in an interview a while ago, we are at a moment where the technology is spilling out of the lab and into the world. Time to strap up then!

(With inputs from Human Compatible: AI and the Problem of Control by Stuart Russell, published by Penguin, UK. Extracted with permission.)

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Doctor’s Hospital focused on incorporation of AI and machine learning – EyeWitness News

NASSAU, BAHAMAS Doctors Hospital has depriortized its medical tourism program and is now more keenly focused on incorporating artificial intelligence and machine learning in healthcare services.

Dr Charles Diggiss, Doctors Hospital Health System president, revealed the shift during a press conference to promote the 2020 Bahamas Business Outlook conference at Baha Mar next Thursday.

When you look at whats happening around us globally with the advances in technology its no surprise that the way companies leverage data becomes a game changer if they are able to leverage the data using artificial intelligence or machine learning, Diggiss said.

In healthcare, what makes it tremendously exciting for us is we are able to sensorize all of the devices in the healthcare space, get much more information, use that information to tell us a lot more about what we should be doing and considering in your diagnosis.

He continued: How can we get information real time that would influence the way we manage your conditions, how can we have on the backend the assimilation of this information so that the best outcome occurs in our patient care environment.

Diggiss noted while the BISX-listed healthcare provider is still involved in medical tourism, that no longer is a primary focus.

We still have a business line of medical tourism but one of the things we do know pretty quickly in Doctors Hospital is to deprioritize if its apparent that that is not a successful ay to go, he said.

We have looked more at taking our specialities up a notch and investing in the technology support of the specialities with the leadership of some significant Bahamian specialists abroad, inviting them to come back home.

He added: We have depriortized medical tourism even though we still have a fairly robust programme going on at our Blake Road facility featuring two lines, a stem cell line a fecal microbiotic line.

They are both doing quite well but we are not putting a lot of effort into that right now compared to the aforementioned.

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Going Beyond Machine Learning To Machine Reasoning – Forbes

From Machine Learning to Machine Reasoning

The conversation around Artificial Intelligence usually revolves around technology-focused topics: machine learning, conversational interfaces, autonomous agents, and other aspects of data science, math, and implementation. However, the history and evolution of AI is more than just a technology story. The story of AI is also inextricably linked with waves of innovation and research breakthroughs that run headfirst into economic and technology roadblocks. There seems to be a continuous pattern of discovery, innovation, interest, investment, cautious optimism, boundless enthusiasm, realization of limitations, technological roadblocks, withdrawal of interest, and retreat of AI research back to academic settings. These waves of advance and retreat seem to be as consistent as the back and forth of sea waves on the shore.

This pattern of interest, investment, hype, then decline, and rinse-and-repeat is particularly vexing to technologists and investors because it doesn't follow the usual technology adoption lifecycle. Popularized by Geoffrey Moore in his book "Crossing the Chasm", technology adoption usually follows a well-defined path. Technology is developed and finds early interest by innovators, and then early adopters, and if the technology can make the leap across the "chasm", it gets adopted by the early majority market and then it's off to the races with demand by the late majority and finally technology laggards. If the technology can't cross the chasm, then it ends up in the dustbin of history. However, what makes AI distinct is that it doesn't fit the technology adoption lifecycle pattern.

But AI isn't a discrete technology. Rather it's a series of technologies, concepts, and approaches all aligning towards the quest for the intelligent machine. This quest inspires academicians and researchers to come up with theories of how the brain and intelligence works, and their concepts of how to mimic these aspects with technology. AI is a generator of technologies, which individually go through the technology lifecycle. Investors aren't investing in "AI, but rather they're investing in the output of AI research and technologies that can help achieve the goals of AI. As researchers discover new insights that help them surmount previous challenges, or as technology infrastructure finally catches up with concepts that were previously infeasible, then new technology implementations are spawned and the cycle of investment renews.

The Need for Understanding

It's clear that intelligence is like an onion (or a parfait) many layers. Once we understand one layer, we find that it only explains a limited amount of what intelligence is about. We discover there's another layer thats not quite understood, and back to our research institutions we go to figure out how it works. In Cognilyticas exploration of the intelligence of voice assistants, the benchmark aims to tease at one of those next layers: understanding. That is, knowing what something is recognizing an image among a category of trained concepts, converting audio waveforms into words, identifying patterns among a collection of data, or even playing games at advanced levels, is different from actually understanding what those things are. This lack of understanding is why users get hilarious responses from voice assistant questions, and is also why we can't truly get autonomous machine capabilities in a wide range of situations. Without understanding, there's no common sense. Without common sense and understanding, machine learning is just a bunch of learned patterns that can't adapt to the constantly evolving changes of the real world.

One of the visual concepts thats helpful to understand these layers of increasing value is the "DIKUW Pyramid":

DIKUW Pyramid

While the Wikipedia entry above conveniently skips the Understanding step in their entry, we believe that understanding is the next logical threshold of AI capability. And like all previous layers of this AI onion, tackling this layer will require new research breakthroughs, dramatic increases in compute capabilities, and volumes of data. What? Don't we have almost limitless data and boundless computing power? Not quite. Read on.

The Quest for Common Sense: Machine Reasoning

Early in the development of artificial intelligence, researchers realized that for machines to successfully navigate the real world, they would have to gain an understanding of how the world works and how various different things are related to each other. In 1984, the world's longest-lived AI project started. The Cyc project is focused on generating a comprehensive "ontology" and knowledge base of common sense, basic concepts and "rules of thumb" about how the world works. The Cyc ontology uses a knowledge graph to structure how different concepts are related to each other, and an inference engine that allows systems to reason about facts.

The main idea behind Cyc and other understanding-building knowledge encodings is the realization that systems can't be truly intelligent if they don't understand what the underlying things they are recognizing or classifying are. This means we have to dig deeper than machine learning for intelligence. We need to peel this onion one level deeper, scoop out another tasty parfait layer. We need more than machine learning - we need machine reasoning.

Machine reason is the concept of giving machines the power to make connections between facts, observations, and all the magical things that we can train machines to do with machine learning. Machine learning has enabled a wide range of capabilities and functionality and opened up a world of possibility that was not possible without the ability to train machines to identify and recognize patterns in data. However, this power is crippled by the fact that these systems are not really able to functionally use that information for higher ends, or apply learning from one domain to another without human involvement. Even transfer learning is limited in application.

Indeed, we're rapidly facing the reality that we're going to soon hit the wall on the current edge of capabilities with machine learning-focused AI. To get to that next level we need to break through this wall and shift from machine learning-centric AI to machine reasoning-centric AI. However, that's going to require some breakthroughs in research that we haven't realized yet.

The fact that the Cyc project has the distinction as being the longest-lived AI project is a bit of a back-handed compliment. The Cyc project is long lived because after all these decades the quest for common sense knowledge is proving elusive. Codifying commonsense into a machine-processable form is a tremendous challenge. Not only do you need to encode the entities themselves in a way that a machine knows what you're talking about but also all the inter-relationships between those entities. There are millions, if not billions, of "things" that a machine needs to know. Some of these things are tangible like "rain" but others are intangible such as "thirst". The work of encoding these relationships is being partially automated, but still requires humans to verify the accuracy of the connections... because after all, if machines could do this we would have solved the machine recognition challenge. It's a bit of a chicken and egg problem this way. You can't solve machine recognition without having some way to codify the relationships between information. But you can't scalable codify all the relationships that machines would need to know without some form of automation.

Are we still limited by data and compute power?

Machine learning has proven to be very data-hungry and compute-intensive. Over the past decade, many iterative enhancements have lessened compute load and helped to make data use more efficient. GPUs, TPUs, and emerging FPGAs are helping to provide the raw compute horsepower needed. Yet, despite these advancements, complicated machine learning models with lots of dimensions and parameters still require intense amounts of compute and data. Machine reasoning is easily one order or more of complexity beyond machine learning. Accomplishing the task of reasoning out the complicated relationships between things and truly understanding these things might be beyond today's compute and data resources.

The current wave of interest and investment in AI doesn't show any signs of slowing or stopping any time soon, but it's inevitable it will slow at some point for one simple reason: we still don't understand intelligence and how it works. Despite the amazing work of researchers and technologists, we're still guessing in the dark about the mysterious nature of cognition, intelligence, and consciousness. At some point we will be faced with the limitations of our assumptions and implementations and we'll work to peel the onion one more layer and tackle the next set of challenges. Machine reasoning is quickly approaching as the next challenge we must surmount on the quest for artificial intelligence. If we can apply our research and investment talent to tackling this next layer, we can keep the momentum going with AI research and investment. If not, the pattern of AI will repeat itself, and the current wave will crest. It might not be now or even within the next few years, but the ebb and flow of AI is as inevitable as the waves upon the shore.

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The Problem with Hiring Algorithms – Machine Learning Times – machine learning & data science news – The Predictive Analytics Times

Originally published in, December 1, 2019

In 2004, when a webcam was relatively unheard-of tech, Mark Newman knew that it would be the future of hiring. One of the first things the 20-year old did, after getting his degree in international business, was to co-found HireVue, a company offering a digital interviewing platform. Business trickled in. While Newman lived at his parents house, in Salt Lake City, the company, in its first five years, made just $100,000 in revenue. HireVue later received some outside capital, expanded and, in 2012, boasted some 200 clientsincluding Nike, Starbucks, and Walmartwhich would pay HireVue, depending on project volume, between $5,000 and $1 million. Recently, HireVue, which was bought earlier this year by the Carlyle Group, has become the source of some alarm, or at least trepidation, for its foray into the application of artificial intelligence in the hiring process. No longer does the company merely offer clients an asynchronous interviewing service, a way for hiring managers to screen thousands of applicants quickly by reviewing their video interview HireVue can now give companies the option of letting machine-learning algorithms choose the best candidates for them, based on, among other things, applicants tone, facial expressions, and sentence construction.

If that gives you the creeps, youre not alone. A 2017 Pew Research Center report found few Americans to be enthused, and many worried, by the prospect of companies using hiring algorithms. More recently, around a dozen interviewees assessed by HireVues AI told the Washington Post that it felt alienating and dehumanizing to have to wow a computer before being deemed worthy of a companys time. They also wondered how their recording might be used without their knowledge. Several applicants mentioned passing on the opportunity because thinking about the AI interview, as one of them told the paper, made my skin crawl. Had these applicants sat for a standard 30-minute interview, comprised of a half-dozen questions, the AI could have analyzed up to 500,000 data points. Nathan Mondragon, HireVues chief industrial-organizational psychologist, told the Washington Post that each one of those points become ingredients in the persons calculated score, between 1 and 100, on which hiring decisions candepend. New scores are ranked against a store of traitsmostly having to do with language use and verbal skillsfrom previous candidates for a similar position, who went on to thrive on the job.

HireVue wants you to believe that this is a good thing. After all, their pitch goes, humans are biased. If something like hunger can affect a hiring managers decisionlet alone classism, sexism, lookism, and other ismsthen why not rely on the less capricious, more objective decisions of machine-learning algorithms? No doubt some job seekers agree with the sentiment Loren Larsen, HireVues Chief Technology Officer, shared recently with theTelegraph: I would much prefer having my first screening with an algorithm that treats me fairly rather than one that depends on how tired the recruiter is that day. Of course, the appeal of AI hiring isnt just about doing right by the applicants. As a 2019 white paper, from the Society for Industrial and Organizational Psychology, notes, AI applied to assessing and selecting talent offers some exciting promises for making hiring decisions less costly and more accurate for organizations while also being less burdensome and (potentially) fairer for job seekers.

Do HireVues algorithms treat potential employees fairly? Some researchers in machine learning and human-computer interaction doubt it. Luke Stark, a postdoc at Microsoft Research Montreal who studies how AI, ethics, and emotion interact, told the Washington Post that HireVues claimsthat its automated software can glean a workers personality and predict their performance from such things as toneshould make us skeptical:

Systems like HireVue, he said, have become quite skilled at spitting out data points that seem convincing, even when theyre not backed by science. And he finds this charisma of numbers really troubling because of the overconfidence employers might lend them while seeking to decide the path of applicants careers.

The best AI systems today, he said, are notoriously prone to misunderstanding meaning and intent. But he worried that even their perceived success at divining a persons true worth could help perpetuate a homogenous corporate monoculture of automatons, each new hire modeled after the last.

Eric Siegel, an expert in machine learning and author of Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die, echoed Starks remarks. In an email, Siegel told me, Companies that buy into HireVue are inevitably, to a great degree, falling for that feeling of wonderment and speculation that a kid has when playing with a Magic Eight Ball. That, in itself, doesnt mean HireVues algorithms are completely unhelpful. Driving decisions with data has the potential to overcome human bias in some situations, but also, if not managed correctly, could easily instill, perpetuate, magnify, and automate human biases, he said.

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The 4 Hottest Trends in Data Science for 2020 – Machine Learning Times – machine learning & data science news – The Predictive Analytics Times

Originally published in Towards Data Science, January 8, 2020

2019 was a big year for all of Data Science.

Companies all over the world across a wide variety of industries have been going through what people are calling a digital transformation. That is, businesses are taking traditional business processes such as hiring, marketing, pricing, and strategy, and using digital technologies to make them 10 times better.

Data Science has become an integral part of those transformations. With Data Science, organizations no longer have to make their important decisions based on hunches, best-guesses, or small surveys. Instead, theyre analyzing large amounts of real data to base their decisions on real, data-driven facts. Thats really what Data Science is all about creating value through data.

This trend of integrating data into the core business processes has grown significantly, with an increase in interest by over four times in the past 5 years according to Google Search Trends. Data is giving companies a sharp advantage over their competitors. With more data and better Data Scientists to use it, companies can acquire information about the market that their competitors might not even know existed. Its become a game of Data or perish.

Google search popularity of Data Science over the past 5 years. Generated by Google Trends.

In todays ever-evolving digital world, staying ahead of the competition requires constant innovation. Patents have gone out of style while Agile methodology and catching new trends quickly is very much in.

Organizations can no longer rely on their rock-solid methods of old. If a new trend like Data Science, Artificial Intelligence, or Blockchain comes along, it needs to be anticipated beforehand and adapted quickly.

The following are the 4 hottest Data Science trends for the year 2020. These are trends which have gathered increasing interest this year and will continue to grow in 2020.

(1) Automated Data Science

Even in todays digital age, Data Science still requires a lot of manual work. Storing data, cleaning data, visualizing and exploring data, and finally, modeling data to get some actual results. That manual work is just begging for automation, and thus has been the rise of automated Data Science and Machine Learning.

Nearly every step of the Data Science pipeline has been or is in the process of becoming automated.

Auto-Data Cleaning has been heavily researched over the past few years. Cleaning big data often takes up most of a Data Scientists expensive time. Both startups and large companies such as IBM offer automation and tooling for data cleaning.

Another large part of Data Science known as feature engineering has undergone significant disruption. Featuretools offers a solution for automatic feature engineering. On top of that, modern Deep Learning techniques such as Convolutional and Recurrent Neural Networks learn their own features without the need for manual feature design.

Perhaps the most significant automation is occurring in the Machine Learning space. Both Data Robot and H2O have established themselves in the industry by offering end-to-end Machine Learning platforms, giving Data Scientists a very easy handle on data management and model building. AutoML, a method for automatic model design and training, has also boomed over 2019 as these automated models surpass the state-of-the-art. Google, in particular, is investing heavily in Cloud AutoML.

In general, companies are investing heavily in building and buying tools and services for automated Data Science. Anything to make the process cheaper and easier. At the same time, this automation also caters to smaller and less technical organizations who can leverage these tools and services to have access to Data Science without building out their own team.

(2) Data Privacy and Security

Privacy and security are always sensitive topics in technology. All companies want to move fast and innovate, but losing the trust of their customers over privacy or security issues can be fatal. So, theyre forced to make it a priority, at least to a bare minimum of not leaking private data.

Data privacy and security has become an incredibly hot topic over the past year as the issues are magnified by enormous public hacks. Just recently on November 22, 2019, an exposed server with no security was discovered on Google Cloud. The server contained the personal information of 1.2 Billion unique people including names, email addresses, phone numbers, and LinkedIn and Facebook profile information. Even the FBI came in to investigate. Its one of the largest data exposures of all time.

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