A leading figure in the field of machine intelligence,Refik Anadolsimmersive environments are the result of groundbreaking research, and the idea of revealing the invisible elements of nature and architecture to the naked eye. His award-winning, eponymous studio has given life to projects in over 50 cities that have been experienced by millions. How have our lives changed since the introduction of artificial intelligence? How has our relationship with space evolved? How can we, as human beings, become smarter through these technologies? These questions are at the heart of Refiks sensorial installations, which dissolve the barriers that restrict the imagination. In October 2021, the Turkish-American new media artist showcased a Middle Eastern reboot of Quantum Memories at Abu Dhabis Semi Permanent festival, a site-specific public art installation that creates a hybrid relationship between physical and subjective realities.

Your abstract installations explore the relationship between architecture and media art. How does this relate to your experience of the modern world?

Since I was about eight years old, my mother would say that I was looking at windows, doors, the floor and the ceiling of my living room, and criticising why its always the same. Why was everything so similar? My idea of embedding media art into architecture using data, to give life meaning, came when I was a child.

I think in the last ten years, my journey with data and architecture grew more and more. I simply use my work as a canvas to depict the imagination. So its pretty much my real-life scenario. Any city in the world has the means of becoming a canvas for artists. Thats my vision in general. I design immersive environments to create experiences, especially with artificial intelligence and data.

What can you reveal about your installation at Semi Permanent Middle East 2021, showcased in October?

My installation at Semi Permanent was calledQuantum Memories. This was a very inspiring pandemic project as they say. In 2019, I got the commission from the National Gallery of Victoria for their 2020 Triennial. During the same year, I had a call with the Google Quantum AI team, who are behind one of the most inspiring quantum researches ever done in history, called Quantum Advantage Research.

They were able to make a quantum computer to create almost impossible computational tests that could be done in a couple of minutes versus how long it would take a traditional computer which could be hours or days! It was a major breakthrough and that was one of the most inspiring things for me in technology.

Theres also a very inspiring study done by Hugh Everett III. He was a professor and a quantum physicist in the very early 60s and 70s. Through his work, I learned this idea that everything that happens in the world shows us alternative dimensions.

You specialise in machine intelligence. How would you define this?

I think, at the moment, it is an extension of the human mind, instead of a part of the human mind, meaning that machines do not forget, but they also do not have a conscious mind yet. So, machines do not decide to decide, basically. In my practice I found that machine intelligence allowed me to use a brush that can paint, a brush that can remember, a brush that can hallucinate, and eventually, a brush that can dream.

AI can be a powerful tool. A tool that can enhance our memory, learning, dreaming, imagining, and many other aspects of the human cognitive capacity. I do believe that when we have a purpose, by using machine intelligence we are given a profound chance of creating much more impactful and meaningful research and eventually, a life-changing moment may happen. I think other fields will change because of this, too. This has already happened in art, science and many other disciplines.

What interests you about exploring space and data?

I have actually been using data in my work since 2009. I was experimenting with early sensory data. Then, ten years ago, I made my very first data sculpture by using acoustic data. Over the years I explored a lot of information, but I had to be ethical and made it a point to never use personal data.

Clearly, I am focusing on collective memories or collective data from open sources, cultural archives, architectural domains, or even nature and time. For the last two years, I have been exploring nature. I think we just need to be more careful about nature in general.

Your site-specific, dreamlike environments invite viewers to envision new realities and question traditional interior and architectural possibilities. Take us back to how all of this started.

This started from my imagination as a child. Ive always believed that life can be the most divine, magical experience, and using data and AI has allowed me to create these experiences. I could never just forget or ignore the beauty of life and its connection with real environments. I am trying to create immersive environments with several scenarios.

Ive found that people completely enjoy when they are detached from reality, even if just for a moment. They completely enjoy their perception when its enhanced by AI the sounds, visual data, and even scent. Recently, I was able to use real-time scent augmentation in one of our projects, which was showcased in Milan. Im constantly exploring this idea as much as I can to bring fresh perspectives and push beyond the physical quality of life.

How do you believe our experience of space is changing after the introduction of smartphones and other technologies?The first thing we see in the morning is most likely a smartphone, and its also the last thing we see at night. Our life is in between these two moments. So what we see, eat, hear, where we go, what we buy, what we read and watch, is most likely defined by various algorithms.

We are living in a world in which Im feeling a sense of displacement, a feeling that we are kind of detaching from reality and physical quality of life. We are living in a virtual space. I believe this virtual space is a metaverse, a new dimension for the mind. I do believe that our DNA and definition of genius is constantly changing. Our brains are always changing and adapting to the new reality.

This is really interesting to me how much change is happening, and how we can understand this change. Can we learn something from this? Are we getting better in certain conditions? Or are we getting worse in certain conditions? Should we be much more aware of it?

Im certain that a consciousness change is happening through the powerful shift of hardware and software that engages with humanity.

What challenges do you think we encounter as humans living in the age of AI?There are two problems: free will and priority. Now, its pretty clear that machines are predicting, every day. So are we making the best decisions? Or could we do better? These are the questions I ask a lot. Also from another perspective, what do we do with these machines that are learning algorithms? Do we just imagine scenarios? Can we use them creatively? Can we ask the questions we want? Can we use the same tools to enhance our cognitive functions? I really think important questions like these will ultimately change the evolution of humanity.

What was the intention behind the multimedia work Wind of Dubai?Wind of Dubaiwas an exciting project displayed in the lobby of Hotel Indigo in Downtown Dubai. It began when I started making a data painting around 2009. I believe that every city has very unique patterns, temperatures, air pressure and air quality. Wind is one of the most inspiring things for me its basically the sculpture of nature. The whole idea was to try and capture that poetry in motion. The aim for this artwork was to create a calm and relaxing experience leading to an emotional attachment.

Walk us through your process of translating technology into spatial design.In my practice, Im not just creating a bunch of shiny pixels. Im doing my very best to bring depth to surface by understanding exactly why technology may be purposeful and impactful for my imagination. Not because its the hype, or because its new, or because it is popular.

In my team of three architects, we are not only designing spaces but also the technology. We are all cohesively imagining a sensual and experiential layer at the same time. So instead of just clicking on an image of a building, we are more likely truly working with the data, algorithms and the architecture at the same time, to make it different and fresh.

So whats next for you?

I was very excited to come to Abu Dhabi, it was actually my first time in the region and Im very excited to share my work with everyone. Im also excited to share our upcoming NFT projects, which youll hear very soon about.

Refikanadolstudio.com

Lead image caption credits: Refik Anadol Studio. Machine Hallucinations Space:Metaverse NFT Collection(render). 2021

From Harpers Bazaar Saudi Winter 2021 issue

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Refik Anadol On The Experience Of The Modern World, Machine Learning And Wind Of Dubai - HarpersBazaarArabia

A recent study by Gartner indicates that by 2025 the 10% of enterprises that establish Machine Learning (ML) or Artificial Intelligence (AI) engineering best practices will generate at least three times more value from their AI and ML efforts than the 90% of enterprises that don't. With such a high value estimated to be derived only from the adoption of ML/AI practices, it is difficult to not agree that the future of enterprises rests heavily on AI and ML technologies with other digital technologies.The pandemic has unveiled a world that embraced technology at a pace that would have otherwise taken ages to evolve.

Traditional practices that saw monolithic systems, lack of flexibility and manual processes were all blocking innovation.

Also Read:Artificial Intelligence: A Pathway to success for enterprises

However, mass new-age technology acceptance induced by the pandemic has helped enterprises overcome these challenges. Modern technologies like AI and ML are opening a new world of possibilities for organisations.

Seizing the early-mover advantage will particularly benefit organisations in taking important business decisions in a more informed, intuitive way.

The applicability of new-age technologies is growing every day. For example, marketers are starting to use ML-based tools to personalise offers to their customers and further measure their satisfaction levels through the successful implementation of ML algorithms into their operations.

This and there are more examples of how AI/ML algorithms are enabling organisations run their businesses smartly and make them profitable.Additionally, enterprises are recognising the benefits of cloud infrastructure and applications with ML and AI algorithms built in.

They allow companies to spend less time on manual work and management and instead focus on high-value jobs that drive business results. ML can result in efficiencies in workloads of enterprise IT and ultimately reduce IT infrastructure costs.

This stands especially true in India, where consulting firm Accenture estimates in one of its reports that the use of AI could add $957 billion to the Indian economy in 2035 provided the "right investments" are made in new-age technology. India, with its entrepreneurial spirit, abundance of talent and the right sources of education has mega potential to unleash AI's true capabilities - but they need the right partner.

The biggest limitation in using AI is that companies often run into implementation issues which could be anything from scarcity of data science expertise to making the platform perform in real-time.

As a result, there is slight reluctance in accepting AI among organisations, and this, in turn, is leading to inconsistencies and lack of results.

Also Read:Three ways AI can help transform businesses

However, with the right partner, India's true potential can be harnessed. As we move into an AI/ML led world, we need to lead the change by building the requisite skills.

While many companies don't have enough resources to marshal an army of data science PhDs, a more practical alternative is to build smaller and more focused "MLOps" teams - much like DevOps teams in application development.

Such teams could consist of not just data scientists, but also developers and other IT engineers whose mission would be to deploy, maintain, and constantly improve AI/ML models in a production environment. While there is a huge responsibility lying on IT professionals to develop an AI/ML led ecosystem in India, companies must also align resources to help them be successful. In due course, AI/ML will be the competitive advantage that companies will need to adopt in order to stay relevant and sustain businesses.

Forrester predicts that one in five organisations will double down on "AI inside" - which is AI and ML embedded in their systems and operational practices.

AI and ML are powerful technology tools that hold the key to achieving an organization's digital transformation goals.

(The author is Head-Technology Cloud, Oracle India.)

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How leveraging AI and machine learning can give companies a competitive edge - Business Today

Machine Learning and Its Application: A Quick Guide for Beginners aims to cover most of the core topics required for study in machine learning curricula included in university and college courses. The textbook introduces readers to central concepts in machine learning and artificial intelligence, which include the types of machine learning algorithms and the statistical knowledge required for devising relevant computer algorithms. The book also covers advanced topics such as deep learning and feature engineering.

Key features:

- 8 organized chapters on core concepts of machine learning for learners

- Accessible text for beginners unfamiliar with complex mathematical concepts

- Introductory topics are included, including supervised learning, unsupervised learning, reinforcement learning, and predictive statistics

- Advanced topics such as deep learning and feature engineering provide additional information

- Introduces readers to python programming with examples of code for understanding and practice

- Includes a summary of the text and a dedicated section for references

Machine Learning and Its Application: A Quick Guide for Beginners is an essential book for students and learners who want to understand the basics of machine learning and equip themselves with the knowledge to write algorithms for intelligent data processing applications.

About the Author:

Dr. Indranath Chatterjee is a Professor in the Department of Computer Engineering at Tongmyong University, Busan, South Korea. He received his Ph. D. in Computational Neuroscience from the Department of Computer Science, University of Delhi, India. His research areas include Computational Neuroscience, Schizophrenia, Medical Imaging, fMRI, and Machine learning. He has authored and edited 8 books on Computer Science and Neuroscience published by renowned international publishers. To date, he has published numerous research papers in international journals and conferences. He is a recipient of various global awards on neuroscience. He is currently serving as a Chief Section Editor of a few renowned international journals and serving as a member of the Advisory board and Editorial board of various international journals and Open-Science organizations worldwide. He is presently working on several projects of government & non-government organizations as PI/co-PI, related to medical imaging and machine learning for a broader societal impact, in collaboration with several universities globally. He is an active professional member of the Association of Computing Machinery (ACM, USA), Organization of Human Brain Mapping (OHBM, USA), Federations of European Neuroscience Society (FENS, Belgium), Association for Clinical Neurology and Mental Health (ACNM, India), and International Neuroinformatics Coordinating Facility (INCF, Sweden).

Keywords:

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For more information, please visit: https://bit.ly/32pIrU7

Disclaimer: AAAS and EurekAlert! are not responsible for the accuracy of news releases posted to EurekAlert! by contributing institutions or for the use of any information through the EurekAlert system.

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Machine knowledge and its applications - EurekAlert

Machine Learning in Education Market study by jcmarketresearch.com provides details about the market dynamics affecting the market, Market scope, Market segmentation and overlays shadow upon the leading market players highlighting the favorable competitive landscape by top majorIBM, Microsoft, Google, Amazon, Cognizan, Pearson, Bridge-U, DreamBox Learning, Fishtree, Jellynote, Quantum Adaptive Learning and trends prevailing over the years.

The research report provides deep insights into the global market revenue, parent market trends, macro-economic indicators, and governing factors, along with market attractiveness per market segment. The report provides an overview of the growth rate of the Machine Learning in Education market during the forecast period, i.e., 20212030. Most importantly, the report further identifies the qualitative impact of various market factors on market segments and geographies. The research segments the market to offer more clarity regarding the industry, the report takes a closer look at the current status of various factors including but not limited to supply chain management, niche markets, distribution channel, trade, supply, and demand and production capability across different countries.

The Machine Learning in Educationreport profiles the key players in the industry, along with a detailed analysis of their individual positions against the global landscape. The study conducts SWOT analysis to evaluate strengths and weaknesses of the key players IBM, Microsoft, Google, Amazon, Cognizan, Pearson, Bridge-U, DreamBox Learning, Fishtree, Jellynote, Quantum Adaptive Learning in the Machine Learning in Education market. The researcher provides an extensive analysis of the Machine Learning in Education market size, share, trends, overall earnings, gross revenue, and profit margin to accurately draw a forecast and provide expert insights to investors to keep them updated with the trends in the market.

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Competitive scenario:

The Machine Learning in Education study assesses factors such as segmentation, description, and applications of Machine Learning in Education industries. It derives accurate insights to give a holistic view of the dynamic features of the business, including shares, profit generation, thereby directing focus on the critical aspects of the business.

The final report will add the analysis of the Impact of Covid-19 in this Machine Learning in Education report Market.

Adapting to the recent novel COVID-19 pandemic, the impact of the COVID-19 pandemic on the global Machine Learning in Education Marketis included in the present report. The influence of the novel coronavirus pandemic on the growth of the Machine Learning in Education Market is analyzed and depicted in the report.

Some of the companies competing in the Machine Learning in Education Market are

IBM, Microsoft, Google, Amazon, Cognizan, Pearson, Bridge-U, DreamBox Learning, Fishtree, Jellynote, Quantum Adaptive Learning

Segment by Type Cloud-Based On-PremiseSegment by Application Intelligent Tutoring Systems Virtual Facilitators Content Delivery Systems Interactive Websites Others

Segmentation

The Machine Learning in Education Market has been segmented on the basis of different aspects. The market is also segmented according to region. The Machine Learning in Education Market has been segmented into Latin America, North America, Asia Pacific, Europe, and the Middle East & Africa on the basis of region

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Research Methodology

The Machine Learning in Education report has its roots definitely set in thorough strategies provided by the proficient data analysts. The research methodology involves the collection of information by analysts only to have them studied and filtered thoroughly in an attempt to provide significant predictions about the market over the review period. The Machine Learning in Education research process further includes interviews with leading market influencers, which makes the primary research relevant and practical. The secondary method gives a direct peek into the demand and supply connection specifically into Machine Learning in Education market. The Machine Learning in Education market methodologies adopted in the report offer precise data analysis and provides a tour of the entire market. Both primary and secondary approaches to data collection have been used. In addition to these, publicly available sources such as SEC filings, annual reports, and white papers have been used by data analysts for an insightful understanding of the Machine Learning in Education market. The research methodology clearly reflects an intent to extract a comprehensive view of the market by having it analyzed against many parameters. The valued inputs enhance the Machine Learning in Education report and offer an edge over the peers.

Drivers & Constraints

The Machine Learning in Education Market rests united with the incidence of leading top IBM, Microsoft, Google, Amazon, Cognizan, Pearson, Bridge-U, DreamBox Learning, Fishtree, Jellynote, Quantum Adaptive Learning players who keep funding to the markets growth significantly every year. The report studies the value, volume trends, and the pricing structure of the market so that it could predict maximum growth in the future. Besides, various suppressed growth factors, restraints, and opportunities are also estimated for the advanced study and suggestions of the market over the assessment period.

Machine Learning in Education Market Segmented by Region/Country: North America, Europe, Asia Pacific, Middle East & Africa, and Central & South America

Interested in purchasing this full in-depth Machine Learning in Education Report? Click here for instant [emailprotected] jcmarketresearch.com/checkout/1495067

Thanks for reading this release; you can also customize this report to get select chapters or region-wise coverage with regions such as Asia, North America, and Europe.

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Machine Learning in Education Market to Witness Astonishing Growth by 2030 Industrial IT - Industrial IT

After 20 years working in software engineering across a variety of industries and government departments, Darragh Sherwin, a development team lead at Overstock Ireland, and a current student at IT Sligo, noticed that he had a skills gapcompared to his colleagues, following a move to a different department within Overstock.

My team recently moved into the algorithms department of Overstock which employs a lot of data scientists, machine learning (ML) scientists and ML engineers. The move highlighted a skills deficit that I had in Data Science, I had always had it in the back of my head to continue further education and studying the part-time, online Masters in Data Science at IT Sligo felt like a natural alignment. I was moving into a department where data is crucial and there are moves across all industries is to be more data driven,says Sherwin.

Data Science is now the backbone of any industry and current trends indicate that it will accumulate even more importance in the coming years. If businesses want to succeed, it is critical that they bank on data science to make data-informed decisions based on insights and trends.

Due to the ever-growing importance of data, data scientists are now in high demand and IT Sligo are helping to close the current skills deficit in data science with their part-time, online Masters in Data Science.

The course includes a combination of statistical analysis, modelling, machine learning and data visualisation. Applicable to any industry, it combines techniques from mathematics, statistics, information theory, computer science and artificial intelligence. Automated driving, consumer buying habits, medical imaging, business intelligence, fraud/risk detection and speech recognition are just a few applications. Masters students will design data analytic techniques, interpret, and manage big data using software as well as machine learning, and probabilistic and statistical methods.

On why he chose the course from IT Sligo, Sherwinsays; Overstock has worked closely with IT Sligo, we hire graduates from IT Sligo and they are always of impressively high calibre. The course modules aligned with my understanding of the area and gives a good foundation to students.

Relevant to engineers who require upskilling in data science or those have already qualified with a Level 8 honours degree in Computer Science (or related disciplines), this masters is offered part-time and online with live lectures in the evening. You can study anywhere and in your own time.

It is great to have the flexibility of studying online. I have a young child so if I need to miss a lecture, I can come back and watch it later. There are great online resources for learning and college seems to have paid particular attention to ensuring online studying is very smooth with their tools like Moodle and Microsoft Teams, says Sherwin. Choosing to study a part-time, online course allows students to upskill for their career while also working full-time.

A qualification in Data Science skills can lead to an exciting career in an array of industries including IT, financial services, retail, and manufacturing.These roles are not confined to IT based positions but can lead to roles in business intelligence, analysts, or data warehouse consultants.The rewards for such positions are also inviting, starting salaries range from 40,000 with senior data scientists commanding annual salaries of more than 100,000.

Online Learning at IT Sligo is ranked number onefor Most Flexible Learning Students in the Good University Guide 2021. With more than 150 online courses available, IT Sligo is Irelands leading online provider. Through innovative online teaching methods, students anywhere in the world can study and graduate with fully accredited online qualifications matched to industry demand.

Applications are now open for the Masters in Data Science at IT Sligo, starting part-time, online on Monday, January 17th, 2022. Apply here - http://www.itsligo.ie/datascience

Continued here:
IT Sligo helping to close the data science skills gap with flexible, online learning - The Irish Times

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Start a lucrative new tech career in 2022 by easily learning how to code for less than $50 - ZDNet

Pennies. Not even once.

Whether its asking Siri what 0/0 is or saying, Hey Google, lets play Madlibs, adults have already gotten comfortable with the idea that AI can be a go-to when curiosity or boredom hit. Children are getting in on the fun too, but at what cost? In a recent event gone viral, a young boy asked Alexa if reindeer can fly. It responded with a curt No. In a blazingly quick display of deduction, the boy is shocked to discover this must mean Santa isnt real! His dad shames Alexa, tells his kid a noble lie, and everyones spirit slow claps.

But this isnt the only time Alexa got in trouble for potentially shocking children. A 10-year-old girl who was fighting some boredom with AI asked Alexa for a challenge to do. I get that climbing Mount Everest would have been a taxing suggestion to give to most 10-year-olds, but Alexas suggestion for her to try her hand at a Static Shock cosplay wasnt much better if were being honest. This is not all Alexas fault humans came up with the Penny Challenge, after all but this is something that was begging to happen eventually. We need to stop giving potentially deadly actions cute names. For example, if Alexa were to recommend the Chubby Bunny challenge, an adult who happened to be in the room might just hear the adorable name and look past the very clear asphyxiation risk the game poses to children.

Given how tumultuous this year has been, I personally cannot rule out that this was AIs first attack on our youth. That said, it was probably just an (innocent?) machine learning error. Amazon promptly fixed this feature, but it does make me wonder how culpability would have played out in the courtroom. Surely there wouldnt have been Ford Pinto levels of blame, but as self-driving cars and other shot-calling devices spread, were gonna need to determine how we hold algorithms (and those who code or release them) accountable. Most of us want the fruits of what machine learning can give us, but what happens when a computers educated guess is the wrong answer? I dont know, but lets not outsource babysitting to Alexa or Siri in the meantime. The Home Alone series would have been a lot shorter if Kevin took Alexas recommendations when he was looking for time to kill.

Amazon Says It Fixed An Error That Led Alexa To Tell A 10-Year-Old Girl To Put A Penny In An Electrical Outlet [Business Insider]

Chris Williams became a social media manager and assistant editor for Above the Law in June 2021. Prior to joining the staff, he moonlighted as a minor Memelord in the Facebook groupLaw School Memes for Edgy T14s. He endured Missouri long enough to graduate from Washington University in St. Louis School of Law. He is a former boatbuilder who cannot swim,a published author on critical race theory, philosophy, and humor, and has a love for cycling that occasionally annoys his peers. You can reach him by email atcwilliams@abovethelaw.comand by tweet at@WritesForRent.

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Entertaining As Alexa Is, Coloring Books Are Way Less Dangerous - Above the Law

The holiday season means an influx of orders for businesses. It can also mean long wait times for those contacting call centers and customer service agents.

However, this year with the Great Resignation and many leaving call center positions, contact center and customer service agents are scarce.

It's getting harder for organizations to find employees to fill service departments, according to Shawna Wolverton, executive vice president of product at customer experience vendor Zendesk. The vendor provides SaaS products to customer support and sales departments. Wolverton said even when organizations hire new employees, the hires require training, which can take time. During the holidays, organizations need to respond to their customers quickly.

In this Q&A, Wolverton discusses how AI, automation and machine learning (ML) can play a role in helping customer service agents as they deal with the influx of customers, not only during the holidays but also in the future.

What are some of the problems agents are facing in the CX world?

Shawna Wolverton: Long gone are the are the days when customers feel like they had to get on the phone with someone to get an answer. Right now, they want to get their answer fast. They're sort of used to Googling for answers. So, we're really optimizing for that and we're seeing our [users] really want to optimize for that.

It comes down to a bunch of things. One is around automation and this idea of bringing together powerful, conversational experiences that allow customers to get those answers fast. Not necessarily without having to talk to an agent, but freeing up agents who maybe were sort of bogged down in: 'Where is my order, and when will it arrive? Can I reset my password? Can you help me change my reservation?' Being able to self-serve some of those things. Then freeing up those agents for those higher-value, more intensive conversations that you do sometimes need to have that one-on-one time to dig in, person to person.

Long gone are the are the days when customers feel like they had to get on the phone with someone to get an answer. Right now, they want to get their answer fast. Shawna WolvertonExecutive vice president of product, Zendesk

What are some specific ways can AI and ML help free up customer service agents?

Wolverton: One of the most valuable information sets a business has is all those tickets that they've solved before. We're finding great benefit from helping that new agent get the context, not just from the customer, but from all the questions and answers that have come before. So, understanding intent and surfacing that for the agent and then providing suggested responses.

We have the ability for automated responses called macros that we can suggest based on doing some machine learning and detection on the issues that come in and then surfacing the answers, either a help desk article or a previously closed ticket that was solved well, and providing those to customers.

On the other end is really like that sort of full automation and building out a chatbot that allows you to recognize those intents and then give answers automatically to customers sometimes without even having to go to an agent. Then the ability sort of through, you know, conversational APIs that exist to build out the kinds of systems that recognize intent and then actually offer an interactive solution so you can maybe do the password reset in the messaging conversation or do the reservation change without having to talk to an agent.

When you know there's a ton of volume, you can really use some of the machine learning and intent detection to understand how angry a customer is or how to route that issue. If someone's really upset about shipping, you can get them directly to someone who can help with that problem, rather than having to escalate that through multiple lines of agents and getting transferred. It's a much better experience for the end user and then much better experiences for the agents as well.

Is this reliance on automation, machine learning or AI something that will continue to grow in the customer service business?

Wolverton: What's great is that this technology is moving so fast. Even in our own portfolio, our bots used to be able to suggest an article and that was sort of the end of the game.

The more this technology continues to evolve with stronger intention and action with the ability for these bots to have more natural conversations with customers and to learn more and more from tickets that have been solved already, then I think it is going to be a critical part of the growing customer service and customer experience teams.

It's going to be a way that they can differentiate in the marketplace of being able to get great answers to customers even more quickly.

Where do you see this technology going as we go into 2022 and beyond?

Wolverton: I think we're at the beginning of the of the curve here. As this technology advances and develops and becomes democratized more people are going to get the power of this and they're going to see the benefit for their customers and the benefit for their agents. I think we'll continue to see more evolution here and more and more customers will be adopting this kind of AI and ML technology, especially across channels, like messaging, where you have these long-running, ongoing conversations. With this idea of messaging you can easily switch between automated conversations with bots for your quick answers, and hand off easily to agents and those conversations can really live both in an automated and a human-to-human world.

Editor's note:This interview has been edited for clarity and conciseness.

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AI is the game-changer needed in customer service - TechTarget

Computer vision systems sometimes make inferences about a scene that fly in the face of common sense. For example, if a robot were processing a scene of a dinner table, it might completely ignore a bowl that is visible to any human observer, estimate that a plate is floating above the table, or misperceive a fork to be penetrating a bowl rather than leaning against it.

Move that computer vision system to a self-driving car and the stakes become much higher for example, such systems have failed to detect emergency vehicles and pedestrians crossing the street.

To overcome these errors, MIT researchers have developed a framework that helps machines see the world more like humans do reports MIT News. Their new artificial intelligence system for analyzing scenes learns to perceive real-world objects from just a few images, and perceives scenes in terms of these learned objects.

The researchers built the framework using probabilistic programming, an AI approach that enables the system to cross-check detected objects against input data, to see if the images recorded from a camera are a likely match to any candidate scene. Probabilistic inference allows the system to infer whether mismatches are likely due to noise or to errors in the scene interpretation that need to be corrected by further processing.

This common-sense safeguard allows the system to detect and correct many errors that plague the deep-learning approaches that have also been used for computer vision. Probabilistic programming also makes it possible to infer probable contact relationships between objects in the scene, and use common-sense reasoning about these contacts to infer more accurate positions for objects.

If you dont know about the contact relationships, then you could say that an object is floating above the table that would be a valid explanation. As humans, it is obvious to us that this is physically unrealistic and the object resting on top of the table is a more likely pose of the object. Because our reasoning system is aware of this sort of knowledge, it can infer more accurate poses. That is a key insight of this work, says lead author Nishad Gothoskar, an electrical engineering and computer science (EECS) PhD student with the Probabilistic Computing Project.

In addition to improving the safety of self-driving cars, this work could enhance the performance of computer perception systems that must interpret complicated arrangements of objects, like a robot tasked with cleaning a cluttered kitchen.

Gothoskars co-authors include recent EECS PhD graduate Marco Cusumano-Towner; research engineer Ben Zinberg; visiting student Matin Ghavamizadeh; Falk Pollok, a software engineer in the MIT-IBM Watson AI Lab; recent EECS masters graduate Austin Garrett; Dan Gutfreund, a principal investigator in the MIT-IBM Watson AI Lab; Joshua B. Tenenbaum, the Paul E. Newton Career Development Professor of Cognitive Science and Computation in the Department of Brain and Cognitive Sciences (BCS) and a member of the Computer Science and Artificial Intelligence Laboratory; and senior author Vikash K. Mansinghka, principal research scientist and leader of the Probabilistic Computing Project in BCS. The research is being presented at the Conference on Neural Information Processing Systems in December.

A blast from the past

To develop the system, called 3D Scene Perception via Probabilistic Programming (3DP3), the researchers drew on a concept from the early days of AI research, which is that computer vision can be thought of as the inverse of computer graphics.

Computer graphics focuses on generating images based on the representation of a scene; computer vision can be seen as the inverse of this process.Gothoskar and his collaborators made this technique more learnable and scalable by incorporating it into a framework built using probabilistic programming.

Probabilistic programming allows us to write down our knowledge about some aspects of the world in a way a computer can interpret, but at the same time, it allows us to express what we dont know, the uncertainty. So, the system is able to automatically learn from data and also automatically detect when the rules dont hold, Cusumano-Towner explains.

In this case, the model is encoded with prior knowledge about 3D scenes. For instance, 3DP3 knows that scenes are composed of different objects, and that these objects often lay flat on top of each other but they may not always be in such simple relationships. This enables the model to reason about a scene with more common sense.

Learning shapes and scenes

To analyze an image of a scene, 3DP3 first learns about the objects in that scene. After being shown only five images of an object, each taken from a different angle, 3DP3 learns the objects shape and estimates the volume it would occupy in space.

If I show you an object from five different perspectives, you can build a pretty good representation of that object. Youd understand its color, its shape, and youd be able to recognize that object in many different scenes, Gothoskar says.

Mansinghka adds, This is way less data than deep-learning approaches. For example, the Dense Fusion neural object detection system requires thousands of training examples for each object type. In contrast, 3DP3 only requires a few images per object, and reports uncertainty about the parts of each objects shape that it doesnt know.

The 3DP3 system generates a graph to represent the scene, where each object is a node and the lines that connect the nodes indicate which objects are in contact with one another. This enables 3DP3 to produce a more accurate estimation of how the objects are arranged. (Deep-learning approaches rely on depth images to estimate object poses, but these methods dont produce a graph structure of contact relationships, so their estimations are less accurate.)

Outperforming baseline models

The researchers compared 3DP3 with several deep-learning systems, all tasked with estimating the poses of 3D objects in a scene.

In nearly all instances, 3DP3 generated more accurate poses than other models and performed far better when some objects were partially obstructing others. And 3DP3 only needed to see five images of each object, while each of the baseline models it outperformed needed thousands of images for training.

When used in conjunction with another model, 3DP3 was able to improve its accuracy. For instance, a deep-learning model might predict that a bowl is floating slightly above a table, but because 3DP3 has knowledge of the contact relationships and can see that this is an unlikely configuration, it is able to make a correction by aligning the bowl with the table.

I found it surprising to see how large the errors from deep learning could sometimes be producing scene representations where objects really didnt match with what people would perceive. I also found it surprising that only a little bit of model-based inference in our causal probabilistic program was enough to detect and fix these errors. Of course, there is still a long way to go to make it fast and robust enough for challenging real-time vision systems but for the first time, were seeing probabilistic programming and structured causal models improving robustness over deep learning on hard 3D vision benchmarks, Mansinghka says.

In the future, the researchers would like to push the system further so it can learn about an object from a single image, or a single frame in a movie, and then be able to detect that object robustly in different scenes. They would also like to explore the use of 3DP3 to gather training data for a neural network. It is often difficult for humans to manually label images with 3D geometry, so 3DP3 could be used to generate more complex image labels.

The 3DP3 system combines low-fidelity graphics modeling with common-sense reasoning to correct large scene interpretation errors made by deep learning neural nets. This type of approach could have broad applicability as it addresses important failure modes of deep learning. The MIT researchers accomplishment also shows how probabilistic programming technology previously developed under DARPAs Probabilistic Programming for Advancing Machine Learning (PPAML) program can be applied to solve central problems of common-sense AI under DARPAs current Machine Common Sense (MCS) program, says Matt Turek, DARPA Program Manager for the Machine Common Sense Program, who was not involved in this research, though the program partially funded the study.

Additional funders include the Singapore Defense Science and Technology Agency collaboration with the MIT Schwarzman College of Computing, Intels Probabilistic Computing Center, the MIT-IBM Watson AI Lab, the Aphorism Foundation, and the Siegel Family Foundation.

Republished with permission ofMIT News. Read theoriginal article.

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Machines that see the world more like humans do - Big Think

Ahead: "radically human" technologies.

The late author and futurist John Naisbitt called it high tech/high touch. Meaning that the success of any technology depends on how deeply it shapes the human experience. Weve seen Naisbitts law in action with the likes of Facebook and Instagram, of course, and the law applies to enterprise technology efforts as well. The more technology-driven and data-driven organizations become, the more their success depends on the people involved.

The most technologically saturated organization in the world will stumble if it doesnt have innovators or entrepreneurs directing the technology to make lives better for customers, or workplaces better for employees. For example, there is now an abundance of cheap video technology capable of making high-quality films. But it takes inspired humans a Steven Spielberg, Spike Lee, or Ron Howard to make the films magical experiences that make a difference in the world. Likewise, it takes inspired humans to make great companies that improve the world. They master technology, and they master the business opportunities before them.

The rise of high-tech/high-touch enterprises is explored byPaul DaughertyandH. James Wilson, both with Accenture, in their forthcoming book, "Radically Human: How New Technology is Transforming Business and Shaping Our Future."

Daugherty and Wilson point to three indicators of how business and technology are converging, urging executives and managers to take an activist role in building a potentially very productive and rewarding relationship:

All companies are technology companies. Now-ubiquitous intelligent technologies are not only remaking processes, they are also opening new sources of value, underpinning new business and operating models, addressing some of the most intractable business and social challenges, and moving leaders to see technology and strategy as inseparable, they state. That exponential expansion from processes to products, algorithms to architecture, strategy to sustainability is the new reality for all organizations, no matter what business they are in.

Technology has proven to be a force for rapid innovation. The Covid crisis wakened a sleeping giant, Daugherty and Wilson opine. Enterprises everywhere pivoted quicker than they believed they could and demonstrated the adaptability, innovation, and agility that many mistakenly thought theyd already achieved. Major shifts that were predicted to materialize in years happened in months: industry convergence, localized supply chains, and mass virtualization. A great many companies now know they can change faster than they, or anyone, believed possible.

Technology success stories are human success stories. Successful business technology solutions not only push boundaries, but also take on a distinctly human character, Daugherty and Wilson state. As peoples skills, experiences, and, in some cases, humanity evolve in tune with new technologies, the technologies and their design will need to further adapt. And as they adapt, individual and collective capabilities and perspectives will further evolve. They call this convergence and adaptation radically human technology. Examples include technologies such as natural language processing, computer vision, voice recognition, and machine learning... making human interaction with them easier and more efficient," leading to natural conversation, simple touches, and abundant personalization."

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High Tech/High Touch: The More We Rely On Machines, The More We Need Humans - Forbes