Artificial Intelligence Is Developing A Sense Of Smell: What Could A Digital Nose Mean In Practice?

We already know we can teach machines to see. Sensors enable autonomous cars to take in visual information and make decisions about what to do next when they’re on the road. But did you know machines can smell, too?

Aryballe, a startup that uses artificial intelligence and digital olfaction technology to mimic the human sense of smell, helps their business customers turn odor data into actionable information.

You’d be surprised how many practical use cases there are for technology like this. I interviewed Sam Guillaume, CEO of Aryballe, and asked him how digital olfaction works, how it’s currently being used on the market, and what his predictions are for the future of fragrance tech.

How the Nose Knows

Our human noses work by processing odor molecules released by organic and inorganic objects. When energy in objects increases (through pressure, agitation, or temperature changes), odors evaporate, making it possible for us to inhale and absorb them through our nasal cavities.

The odors then stimulate our nasal olfactory neurons and the olfactory bulb. Our brains pull together other information (like visual cues and memories of things we’ve smelled before) to identify the smell and decide what to do next.

You can watch my full interview with Aryballe CEO Sam Guillaume here:

Digital olfaction mimics the way humans smell by capturing odor signatures using biosensors, then using software solutions to analyze and display the odor data. Artificial Intelligence (AI) interprets the signatures and classifies them based on a database of previously collected smells.

“Over the last few years, technology has allowed us to essentially duplicate the way human olfaction actually works,” Guillaume says. “And by porting this technology to readily available techniques like semiconductors, for instance, we can make sensors that are small, convenient, easy to use, and cheap. In terms of performance and its ability to discriminate between smells, it’s pretty close to the way your nose works.”

Practical Use Cases for Digital Olfaction

So how does all this digital olfaction data turn into valuable insights for companies?

Odor analytics can help companies do things like:

●    Engineer the perfect “new car” smells in the automotive industry

●    Predict when maintenance needs to be done in industrial or automotive equipment

●    Automatically detect food spoilage in consumer appliances

●    Reject or approve raw material supply

●    Reduce R&D time for new foods and beverages

●    Ensure fragrances of personal care products like deodorants and shampoos last for a long time

●    Give riders peace of mind on public transportation by emitting an ambient smell

●    Create personal care devices and health sensors that use odors to detect issues and alert users

Leveraging the Power of Odor Data

In the future, companies like Aryballe will potentially be collaborating on projects that will create digital odor libraries for companies, or even creating devices that help COVID-19 patients recover their sense of smell.

Look for more advances as we can continue to find ways to teach computers how to sense the world around them and use the data they collect to help us in our everyday lives.

Find out more about Aryballe’s technology here, and learn more about machine learning and artificial intelligence on my blog.

Bernard Marr is an internationally best-selling author, popular keynote speaker, futurist, and a strategic business & technology advisor to governments and companies. He helps organisations improve their business performance, use data more intelligently, and understand the implications of new technologies such as artificial intelligence, big data, blockchains, and the Internet of Things. Why don’t you connect with Bernard on Twitter (@bernardmarr), LinkedIn (https://uk.linkedin.com/in/bernardmarr) or instagram (bernard.marr)?

Source: Artificial Intelligence Is Developing A Sense Of Smell: What Could A Digital Nose Mean In Practice?

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3 Ways Elearning Is Disrupting the Education Industry

Whether in a formal or informal setting, the ability to conduct research and impart knowledge has always been a crucial part of human life. That led to the establishment of colleges that are now centuries-old and have grown to become institutions. Those institutions might have seemed permanent and too dominant to challenge just a year ago, but the events of 2020 have caused a seismic shift in how people live, and how we learn specifically, such that digital education is clearly going to be the dominant force in the years to come.

This represents a lot of change in the sector, from the kinds of tutors who will be able to succeed in the new model to the tools the students and their teachers will need for an efficient process. That change also represents an immense opportunity for entrepreneurs — to make a profit while making a real impact on the future of humanity.

1. Interactive media

An important aspect of elearning is the ability to use a variety of media in communicating information. While traditional education models were largely restricted to text, and in some cases, audio and video, new digital learning platforms can leverage advanced technologies such as virtual reality (VR) and augmented reality (AR) to create a more immersive learning experience that will help students understand and retain more of the information which they are taught.

It’s also particularly helpful for training, where students gain experience using complex equipment without the risk of making real-life errors. Flight training for pilots and surgery models for surgeons are two increasingly popular applications.

A recent study by showed that education is No. 4 on the list of sectors receiving the largest amount of VR-related investments, shaping the industry to be worth over $700 million by 2025. That would amount to an increase of over 500% in the next five years. As large as those numbers are, they are unsurprising when we consider that up to 97% of surveyed students stated that they would be interested in undergoing a VR educational course. The market will always go where the money is.

2. Flexibility and on-demand education

This is another primary driving factor of the elearning revolution, and it is visible in every aspect of the system. From the fact that students can attend and participate actively in class from anywhere in the world using teleconferencing software, to on-demand classes that allow students to set their own schedules and learn at their own pace, the key is to provide a learning experience that is as tailored to the needs of each student as possible.

This flexibility means that students have full control over their learning process, thus making them more likely to stick to it. Corporate organizations have also been making a push into elearning as part of their training processes. In fact, 41.7% of global Fortune 500 Companies were already using some form of digital training as far back as 2013, and that number has only continued to grow.

This flexibility also reflects in the marketing approach. While traditional institutions are more uptight and reserved, entrepreneurs in the digital learning space can be more engaging by implementing content marketing strategies to attract users, such as the Learn a Course Online course reviews section which helps online learners share their experiences with online courses to help others make the best choices.

3. Artificial intelligence

Education has traditionally been driven by the and student, progressing based on their interactions. Nowadays, advances in artificial intelligence and machine learning/neural networks have resulted in software capable of evaluating how a student is doing based on a variety of criteria and guiding them accordingly to ensure they understand what is being taught fully.

These tools use everything from how students answer quizzes to how long they spend on a page and how many times they look back at certain sections to produce personalized learning plans just for that particular student.

This level of granularity in the education system is unprecedented, giving students the ability to direct their lessons and entire learning experience with the assistance of advanced AI. When combined with the removal of time constraints and a strict curriculum, it is clear why there is such a huge interest in digital learning among teachers and students alike, and why the industry is so ripe for disruption by savvy entrepreneurs.

By: Ademola Alex Adekunbi / Entrepreneur Leadership Network Contributor Founder of Tech Law Info

Source: 3 Ways Elearning Is Disrupting the Education Industry

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What is the Future of Online Education?

Expectations From the Education Sector

Education at scale doesn’t have to suck. If you ditch conventional e-learning’s clicky gimmicks, and focus instead on science-backed design principles and powerful human stories, your training will shift from tedious to transformative. Dr. Aaron Barth, thought-leader and president of Dialectic, gives progressive leaders the confidence they need to tackle their hardest people problems using scientific methods. Rooted in education, Dr. Barth founded Dialectic after completing his Ph.D. in Philosophy at Western University, keen on fusing theory with application.
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Beauty Is In The Brain: AI Reads Brain Data, Generates Personally Attractive Images

Researchers have succeeded in making an AI understand our subjective notions of what makes faces attractive. The device demonstrated this knowledge by its ability to create new portraits on its own that were tailored to be found personally attractive to individuals. The results can be utilised, for example, in modelling preferences and decision-making as well as potentially identifying unconscious attitudes.

Researchers at the University of Helsinki and University of Copenhagen investigated whether a computer would be able to identify the facial features we consider attractive and, based on this, create new images matching our criteria. The researchers used artificial intelligence to interpret brain signals and combined the resulting brain-computer interface with a generative model of artificial faces. This enabled the computer to create facial images that appealed to individual preferences.

“In our previous studies, we designed models that could identify and control simple portrait features, such as hair color and emotion. However, people largely agree on who is blond and who smiles. Attractiveness is a more challenging subject of study, as it is associated with cultural and psychological factors that likely play unconscious roles in our individual preferences. Indeed, we often find it very hard to explain what it is exactly that makes something, or someone, beautiful: Beauty is in the eye of the beholder,” says Senior Researcher and Docent Michiel Spapé from the Department of Psychology and Logopedics, University of Helsinki.

The study, which combines computer science and psychology, was published in February in the IEEE Transactions in Affective Computing journal.

Preferences exposed by the brain

Initially, the researchers gave a generative adversarial neural network (GAN) the task of creating hundreds of artificial portraits. The images were shown, one at a time, to 30 volunteers who were asked to pay attention to faces they found attractive while their brain responses were recorded via electroencephalography (EEG).

“It worked a bit like the dating app Tinder: the participants ‘swiped right’ when coming across an attractive face. Here, however, they did not have to do anything but look at the images. We measured their immediate brain response to the images,” Spapé explains.

The researchers analysed the EEG data with machine learning techniques, connecting individual EEG data through a brain-computer interface to a generative neural network.

“A brain-computer interface such as this is able to interpret users’ opinions on the attractiveness of a range of images. By interpreting their views, the AI model interpreting brain responses and the generative neural network modelling the face images can together produce an entirely new face image by combining what a particular person finds attractive,” says Academy Research Fellow and Associate Professor Tuukka Ruotsalo, who heads the project.

To test the validity of their modelling, the researchers generated new portraits for each participant, predicting they would find them personally attractive. Testing them in a double-blind procedure against matched controls, they found that the new images matched the preferences of the subjects with an accuracy of over 80%.

“The study demonstrates that we are capable of generating images that match personal preference by connecting an artificial neural network to brain responses. Succeeding in assessing attractiveness is especially significant, as this is such a poignant, psychological property of the stimuli.

Computer vision has thus far been very successful at categorising images based on objective patterns. By bringing in brain responses to the mix, we show it is possible to detect and generate images based on psychological properties, like personal taste,” Spapé explains.

Potential for exposing unconscious attitudes

Ultimately, the study may benefit society by advancing the capacity for computers to learn and increasingly understand subjective preferences, through interaction between AI solutions and brain-computer interfaces.

“If this is possible in something that is as personal and subjective as attractiveness, we may also be able to look into other cognitive functions such as perception and decision-making. Potentially, we might gear the device towards identifying stereotypes or implicit bias and better understand individual differences,” says Spapé.

By: University of Helsinki

Source: Beauty is in the brain: AI reads brain data, generates personally attractive images — ScienceDaily

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Anjan Chatterjee uses tools from evolutionary psychology and cognitive neuroscience to study one of nature’s most captivating concepts: beauty. Learn more about the science behind why certain configurations of line, color and form excite us in this fascinating, deep look inside your brain. Check out more TED talks: http://www.ted.com The TED Talks channel features the best talks and performances from the TED Conference, where the world’s leading thinkers and doers give the talk of their lives in 18 minutes (or less). Look for talks on Technology, Entertainment and Design — plus science, business, global issues, the arts and more. Follow TED on Twitter: http://www.twitter.com/TEDTalks Like TED on Facebook: https://www.facebook.com/TED Subscribe to our channel: https://www.youtube.com/TED
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Journal Reference:

  1. Michiel Spape, Keith Davis, Lauri Kangassalo, Niklas Ravaja, Zania Sovijarvi-Spape, Tuukka Ruotsalo. Brain-computer interface for generating personally attractive images. IEEE Transactions on Affective Computing, 2021; 1 DOI: 10.1109/TAFFC.2021.3059043
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What AI Practitioners Could Learn From A 1989 MIT Dissertation

Child at laptop

More than thirty years ago, Fred Davis developed the Technology Acceptance Model (TAM) as part of his dissertation at MIT. It’s one of the most widely cited papers in the field of technology acceptance (a.k.a. adoption). Since 1989, it’s spawned an entire field of research that extends and adds to it. What does TAM convey and how might today’s AI benefit from it?

TAM is an intuitive framework. It feels obvious yet powerful and has withstood the test of time. Davis started with a premise so simple that it’s easy to take it for granted: A person will only try, use and ultimately adopt technology if they are willing to exert some effort. And what could motivate users to expend this effort?

He outlined several variables that could motivate users, and many researchers have added to his list over the years, but these two variables are the ones that were most important: 1. Does it look easy to use? 2. Will it be useful? If the learning curve doesn’t look too steep and there’s something in it for them, a user will be inclined to adopt. Many researchers have added to this foundation over the years. For example, we’ve learned that a user’s intention can also be influenced by subjective norms.

We’re motivated to adopt new tech at work when senior leadership thinks it’s important. Perceived usefulness can also be influenced by image, as in, “Does adopting this tech make me look good?” And lastly, usefulness is high if relevance to the job is high.

TAM can be a powerful concept for an AI practitioner. It should be front-of-mind when embedding AI in an existing tool or process and when developing an AI-first product, as in, one that’s been designed with AI at the center of its functionality from the start. (Think Netflix.) Furthermore, AI can be used to drive adoption by levering TAM principles that increase user motivation.

Making AI more adoptable

With the proliferation of AI in sales organizations, AI algorithms are increasingly embedded in tools and processes leveraged by sales representatives and sales managers. Adding decision engines to assist sales representatives is becoming increasingly common. A sales organization may embed models that help determine a customer’s propensity to buy or churn, recommend next best actions or communications and more. The problem is, many of these initiatives don’t work because of a lack of adoption.

TAM can help us design these initiatives more carefully, so that we maximize the chances of acceptance. For example, if these models surface recommendations and results that fit seamlessly into reps’ tools and processes, they would perceive them as easy to use.

And if the models make recommendations that help a sales person land a new customer, prevent one from leaving and help them upsell or cross-sell when appropriate, reps would perceive them as useful. In other words, if the AI meets employees where they are and offers timely, beneficial support, adoption becomes a no-brainer.

We also see many new products and services that are AI first. For these solutions, if perceived ease of use or perceived usefulness are not high, there would be no adoption. Consider a bank implementing a tech-enabled solution like mobile check deposits. This service depends on customers having a trouble-free experience.

The Newark airport’s global entry system uses facial recognition to scan international flyers’ faces. It’s voluntary, and the experience is fantastic. The kiosk recognizes my face, and a ticket is printed for me to take to the immigration officer. Personally, I find this AI-first process a better experience than the previous system that depended on fingerprints, and now I will always opt for the new one.

Using AI to drive adoption

And perhaps counter intuitively, what if AI was used to drive elements of TAM within existing technology? Can AI impact perceived usefulness? Can AI impact perceived ease of use? Consider CRM. It has been improved and refined over the years and is in use within most sales organizations, yet the level of dissatisfaction with CRM is high and adoption remains a challenge.

How can AI help? A machine learning algorithm that uses location services can recommend that a rep visit a nearby customer, increasing the perceived usefulness of their CRM solution. Intelligent process automation can also help reps see relevant information from a contracting database as information on renewals are being entered. Bots can engage customers on behalf of the representatives to serve up more qualified leads. The possibilities are numerous. All these AI features are designed to ensure that CRM lives up to its promise as a source of value to the sales representative.

Outside of sales, consider patients. In the past few years, many new technologies have been introduced to help diabetics. Adoption of this technology is critical to self-management, and self-management is critical to treating the disease. For any new technology in this space, patients need to see that it’s useful to them.

AI can play a role in gathering information such as glucose levels, activity and food intake and make recommendations on insulin dosing or caloric intake. Such information gathering could go a long way toward reducing the fatigue that diabetics feel while they make countless health and nutrition decisions throughout the day.

AI’s algorithmic nature makes it easy to forget that it’s another technology and that it can aid technology. Its novelty can convince us that everything about it is new. TAM holds up because it’s intuitive, straightforward and proven. While we boldly innovate a path forward in the world of AI, shed convention and think like a disruptor, let’s keep an eye on our history too. There’s some useful stuff in there.

Follow me on Twitter or LinkedIn. Check out my website.

Arun provides strategy and advisory services, helping clients build their analytics capabilities and leverage their data and analytics for greater commercial effectiveness. He currently works with clients on a broad range of analytics needs that span multiple industries, including technology, telecommunications, financial services, travel and transportation and healthcare. His areas of focus are AI adoption and ethics, as well as analytics organization design, capability building, AI explainability and process optimization.

Source: What AI Practitioners Could Learn From A 1989 MIT Dissertation

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The AI Practitioners Guide for Beginners is a series that will provide you with a high-level overview of business and data strategy that a machine learning practitioner needs to know, followed by a detailed walkthrough of how to install and validate one of the popular artificial intelligence frameworks: TensorFlow on the Intel® Xeon® Scalable platform. Read the AI Practitioners Guide for Beginners article:
https://intel.ly/2WQaiE8 Subscribe to the Intel Software YouTube Channel: http://bit.ly/2iZTCsz About Intel Software: The Intel® Developer Zone encourages and supports software developers that are developing applications for Intel hardware and software products. The Intel Software YouTube channel is a place to learn tips and tricks, get the latest news, watch product demos from both Intel, and our many partners across multiple fields.
You’ll find videos covering the topics listed below, and to learn more, you can follow the links provided! Connect with Intel Software: Visit INTEL SOFTWARE WEBSITE: https://intel.ly/2KeP1hD Like INTEL SOFTWARE on FACEBOOK: http://bit.ly/2z8MPFF Follow INTEL SOFTWARE on TWITTER: http://bit.ly/2zahGSn INTEL SOFTWARE GITHUB: http://bit.ly/2zaih6z INTEL DEVELOPER ZONE LINKEDIN: http://bit.ly/2z979qs INTEL DEVELOPER ZONE INSTAGRAM: http://bit.ly/2z9Xsby INTEL GAME DEV TWITCH: http://bit.ly/2BkNshu See also Intel Optimization Notice: https://intel.ly/2HVXVo5 Introduction | AI Practitioners Guide for Beginners | Episode 1 | Intel Software https://www.youtube.com/intelsoftware
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What AI Practitioners Could Learn From A 1989 MIT Dissertation
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We can use legacy adoption principals to drive user behavior for cutting edge AI. We can also use AI to drive adoption in legacy technologies….
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GCHQ | Pioneering a New National Security: The Ethics of Artificial Intelligence
http://www.gchq.gov.uk – February 25
[…] GCHQ has a growing community of data science and AI practitioners and researchers, including an industry-facing AI Lab dedicated to prototyping projects whic […]
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Shingai Manjengwa on LinkedIn: It’s been an incredible week in the ‘Bias in AI’ course at Vector
http://www.linkedin.com – February 25
[…] week in the ‘Bias in AI’ course at Vector Institute – Elliot Creager took the group of SME AI practitioners through codifying bias mathematically and mitigation calculations […]
0
Why most machine learning strategies fail –
bdtechtalks.com – February 25
[…] “As AI practitioners can demonstrate practical examples of how AI can benefit their specific company—leadership wil […]
1
AI For Everyone
http://www.coursera.org – February 25
[…] AIs expert-led educational experiences provide AI practitioners and non-technical professionals with the necessary tools to go all the way from foundational basics […]
1
Why ‘containment rate’ is NOT the best way to measure your chatbot or voicebot •
vux.world – February 25
[…] inbox every week, as well as invites to our weekly live podcast where we interview conversational AI practitioners about the details of how to implement conversational automation and industry trends […]
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Issue 80
[…] Our practices have evolved — and continue to do so — as both society and AI practitioners have come to recognize the importance of privacy […]
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AI: Decoded: Africa calling — Google’s AI HR troubles continue — Facebook’s foray into academia –
http://www.politico.eu – February 24
[…] called on AI conferences to drop Google sponsorship and deny their recruiters access, and for AI practitioners to draft an open letter refusing to work for Google, among other things […]
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A framework for consistently measuring the usability of voice and conversational interfaces •
vux.world – February 23
[…] Don’t forget real users It’s easy for conversational AI practitioners and conversation designers to assume that everyone know how to use voice assistants and chatbots […]
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Events —
http://www.acukltd.com – February 23
[…] LONDON This event offers a great opportunity to meet like minded professionals, MAPP graduates and AI practitioners, researcher Dr Caroly Yousef-Morgan will be providing an update on the newest findings in the field […]
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Artificial Intelligence: Week #7 | 2021
sixgill.com – February 22
[…]   Connect with AI practitioners of all levels Stay connected with artificial intelligence and machine learning practitioners around […]
2
Can We Engineer Ethical AI?
montrealethics.ai – February 22
[…] our next big theme of the discussion appeared, the polemic topic of pushing for licensing for AI practitioners. Licensing AI practitioners Currently, there is no requirement for AI practitioners to be licensed […] observed a lack of understanding within the AI ethics debate on actually being able to tell if AI practitioners are actually complying with the ethical measures established in their place of work (n […]
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Natural Language Processing in TensorFlow
http://www.coursera.org – February 20
[…] AIs expert-led educational experiences provide AI practitioners and non-technical professionals with the necessary tools to go all the way from foundational basics […]
3
すべての人のためのAI【日本語版】
ja.coursera.org – February 20
[…] AIs expert-led educational experiences provide AI practitioners and non-technical professionals with the necessary tools to go all the way from foundational basics […]
1
Deep Learning
http://www.coursera.org – February 20
[…] AIs expert-led educational experiences provide AI practitioners and non-technical professionals with the necessary tools to go all the way from foundational basics […]
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TensorFlow: Advanced Techniques
http://www.coursera.org – February 19
[…] AIs expert-led educational experiences provide AI practitioners and non-technical professionals with the necessary tools to go all the way from foundational basics […]
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GeoWeaver: Improving Workflows for AI and Machine Learning
http://www.uidaho.edu – February 19
[…] GeoWeaver is the open-source workflow management solution that many AI practitioners urgently need […]
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AI in Finance | Online Course by Industry Experts
my.cfte.education – February 18
[…] knowledge on AI PARTICIPANTS WILL ACCESS HIGH QUALITY KNOWLEDGE AND GAIN FIRST HAND EXPERIENCE FROM AI PRACTITIONERS THEMSELVES 01 Welcome to AI in Finance About the course Format of the course and Tips Certificat […]
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Building A Responsible AI Eco-system
analyticsindiamag.com – February 18
[…] ”  This calls for a serious question on Auditing like financial auditing by qualified AI practitioners […] AI or IBM’S Explainable AI  Google’s Model Cards for documentation Deploy diversified team of AI practitioners while developing the models […]
1
ODSC Team Training
odsc.com – February 18
[…] Join the fastest growing network of AI practitioners, sharing knowledge, projects, failures… Team bonding through learning together, interacting wit […]
0
Big data analytics in the cloud with free public datasets
cloud.google.com – February 18
[…] Explore Looker’s blocks here and request a demo to learn more See how a cross-industry team of AI practitioners ramped up data use to fight COVID […]
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Building Ethics Into the Machine Learning Pipeline Tickets, Wed, Feb 17, 2021 at 3:00 PM
[…] about AI ethics education, and has designed original courses, workshops, and frameworks to help AI practitioners learn how to think critically about the ethics of their work […]
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Data Engineering Weekly #29 – Data Engineering Weekly
[…] Google research published a report on data practices in high-stakes AI from interviews with 53 AI practitioners in India, East and West African countries, and the USA […] One of the disrupting read to know 92% of AI practitioners reported experiencing one or more, and 45 […]
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Online AI Course For Business Leaders | AI For Managers Program
[…] combining conceptual understanding with use cases and demos Mentored learning sessions with AI practitioners, focusing on doubt-resolution and case-study based practice Industry case sessions by experts a […] What are “Industry Case Sessions”? Industry case sessions are led by AI practitioners working at a variety of partner companies […]
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Karachi AI – Community of Applied AI Practitioners Public Group | Facebook
http://www.facebook.com – February 14
As I discussed from last couple of weeks? … There a lot of spaces where Semantic Searching Capabilities can help. This wonderful system is made by…
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Unfortunately, Commercial AI is Failing. Here’s Why.
[…] As a practice, AI practitioners must “clean” the data […]
2
News Feature: What are the limits of deep learning?
[…] ” That’s a widely shared sentiment among AI practitioners, any of whom can easily rattle off a long list of deep learning’s drawbacks […]
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Artificial Intelligence: Week #6 | 2021
sixgill.com – February 13
[…] Notable Research Papers: Connect with AI practitioners of all levels Stay connected with artificial intelligence and machine learning practitioners around […]
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Ethics as a service: a pragmatic operationalisation of AI Ethics by Jessica Morley, Anat Elhalal, Francesca Garcia, Libby Kinsey, Jakob Mokander, Luciano Floridi :: SSRN
papers.ssrn.com – February 12
[…] pro-ethical design endeavour rendered futile? And, if no, then how can AI ethics be made useful for AI practitioners? This is the question we seek to address here by exploring why principles and technica […]
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Managing Complex AI Projects | PMI Blog
community.pmi.org – February 11
[…] Capability-building for existing DS/AI practitioners, focusing on the basic work-flow of DS/AI projects […]
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Artificial Intelligence for Ethical Integrity? Questions and Challenges for AI in Times of a Pandemic  –
globaldigitalcultures.org – February 11
[…] can contribute to the imagination of realities and matters of public concern (Milan, 2020) by AI practitioners and policymakers that exist outside their own imaginative faculty […]
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How Andy Jassy Will Lead Amazon’s AI Strategy?
analyticsindiamag.com – February 10
[…] Jassy said all ML experts and AI practitioners get hired in big tech companies, and the low-key enterprises and startups tend to miss out on th […]
1
[2102.02437v1] EUCA: A Practical Prototyping Framework towards End-User-Centered Explainable Artificial Intelligence
arxiv.org – February 10
[…] It serves as a practical prototyping toolkit for HCI/AI practitioners and researchers to build end-user-centered explainable AI […]
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DIU
http://www.diu.mil – February 9
[…] xBD is currently the largest and most diverse annotated building damage dataset, allowing ML/AI practitioners to generate and test models to help automate building damage assessment […]
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Workshops List (AAAI-21) | AAAI 2021 Conference
aaai.org – February 9
[…] (AAAI-21) builds on the success of last year’s AAAI PPAI to provide a platform for researchers, AI practitioners, and policymakers to discuss technical and societal issues and present solutions related to privacy […]
1
What is Responsible AI?. “It’s not artificial intelligence I’m… | by Yash Lara | Analytics Vidhya | Jan, 2021
medium.com – February 8
[…] But there is something called as ‘Ethical AI Practitioners’ […]
1
Playing games, gamification, and the gulf between them
[…] Today, AI practitioners have a rich inventory of hundreds of games, with a myriad of variations […]
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What I Learned From Attending TWIMLcon 2021 —
jameskle.com – February 8
[…] There was a wide range of both technical and case-study sessions curated for ML/AI practitioners […]
0
A Startup’s Journey Towards Artificial Intelligence With AI101 | by Jojo Anonuevo | The Startup | Jan, 2021
medium.com – February 7
[…] I highly recommend these for those who want to be AI practitioners and those tasked to build a team to help them understand the skills needed to recruit and interview […]
0
“Everyone wants to do the model work, not the data work”: Data Cascades in High-Stakes AI –
research.google – February 6
[…] In this paper, we report on data practices in high-stakes AI, from interviews with 53 AI practitioners in India, East and West African countries, and USA […]
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AI is Only Going to Get Smarter. How people are already “cyborgs”… | by Michel Kana, Ph.D | Feb, 2021
michel-kana.medium.com – February 5
[…] a critical mass of AI practitioners […]
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Home
mesumrazahemani.wixsite.com – February 5
Karachi.AI is a premier community of Applied AI practitioners. Founded in 2017, the community has staggering 4000+ members from wide variety of domains.   The unique diversity embodies our vision to educate masses towards Artificial Intelligence and upcoming Machine First era, where Jobs of the future will change drastically. ​ Our vision carries around three pillars of execution: 1. Awareness 2. Engagement 3. Empowerment
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2021 will be the year of MLOps
[…] The implementation of MLOps and closer collaboration of software developers and AI practitioners will bring a maturity to the market in 2021 […]
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What’s with the “Cambrian-AI” theme?
cambrian-ai.com – February 4
[…] Hence, I created Cambrian AI Research, where investors, media, and AI practitioners can keep up with the latest AI innovations, and communicate their plans and innovations, with 100’s […]
0
HPE data science experts help customers navigate the new AI accelerator landscape
community.hpe.com – February 3
[…] AI practitioners want competitive alternatives to CPUs and GPUs […]
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Hands-on Guide to AI Habitat: A Platform For Embodied AI Research –
analyticsindiamag.com – February 3
[…] Unlike the strictly algorithm-led approach of such traditional AI practices, embodied AI practitioners try to first understand the working of biological systems, then develop general principles o […]
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Projects To Know – Issue #67
eepurl.com – February 3
[…] that occur due to data quality issues arising from technical debt) through interviews with 53 AI practitioners across the world. They find that AI practitioners are not properly incentivized to address data quality problems – instead, they are motivated t […]
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AI Strategies and Roadmap: Systems Engineering Approach to AI Development and Deployment | Professional Education
professional.mit.edu – February 3
[…] Communicate your value proposition to stakeholders Receive practical experience from the “voice of AI practitioners” across various industries Formulate a strategic vision and development plan focused on AI products […]
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Designing Ethics Frameworks for AI with Dr. Willie Costello Tickets, Thu, Feb 11, 2021 at 4:00 PM
[…] about AI ethics education, and has designed original courses, workshops, and frameworks to help AI practitioners learn how to think critically about the ethics of their work […]
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Artificial Intelligence: Week #4 | 2021
sixgill.com – February 1
[…] Notable Research Papers: Connect with AI practitioners of all levels Stay connected with artificial intelligence and machine learning practitioners around […]
1
RCV at CVPR 2021
sites.google.com – February 1
[…] policy implications to Consider while constructing representative datasets and training models by AI practitioners […]
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Project Manager — Village Data Analytics (VIDA) | by Nabin Raj Gaihre | Work with TFE Energy | Feb, 2021
medium.com – February 1
[…] The team includes both top-notch AI practitioners, as well as frontier market entrepreneurs with backgrounds in engineering, renewable energy, an […]
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Software engineering intern / Working student / Master thesis | by Nabin Raj Gaihre | Work with TFE Energy | Feb, 2021
medium.com – February 1
[…] The team includes both top-notch AI practitioners, as well as frontier market entrepreneurs with backgrounds in engineering, renewable energy, an […]
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[Proposal] Ocean Academy: Project Oyster �� – Round 2
port.oceanprotocol.com – February 1
[…] series targets business people and organizations dealing with data, data architects, scientists and AI practitioners […]
0
Environmental data justice
http://www.thelancet.com – February 1
[…] there is growing pressure from a community of researchers, activists, and artificial intelligence (AI) practitioners to make Environmental Data Justice (EDJ) a top priority […] One overarching and fundamental concern in the data justice field is the ability of data and AI practitioners to decide what and whose knowledge and data is counted as valid, and what goes ignored an […] As industry and governments increasingly look to AI practitioners and researchers for the solutions to important societal issues, understanding the systemic an […]

Big Ethical Questions about the Future of AI

Artificial intelligence is already changing the way we live our daily lives and interact with machines. From optimizing supply chains to chatting with Amazon Alexa, artificial intelligence already has a profound impact on our society and economy. Over the coming years, that impact will only grow as the capabilities and applications of AI continue to expand.

AI promises to make our lives easier and more connected than ever. However, there are serious ethical considerations to any technology that affects society so profoundly. This is especially true in the case of designing and creating intelligence that humans will interact with and trust. Experts have warned about the serious ethical dangers involved in developing AI too quickly or without proper forethought. These are the top issues keeping AI researchers up at night.

Bias: Is AI fair

Bias is a well-established facet of AI (or of human intelligence, for that matter). AI takes on the biases of the dataset it learns from. This means that if researchers train an AI on data that are skewed for race, gender, education, wealth, or any other point of bias, the AI will learn that bias. For instance, an artificial intelligence application used to predict future criminals in the United States showed higher risk scores and recommended harsher actions for black people than white based on the racial bias in America’s criminal incarceration data.

Of course, the challenge with AI training is there’s no such thing as a perfect dataset. There will always be under- and overrepresentation in any sample. These are not problems that can be addressed quickly. Mitigating bias in training data and providing equal treatment from AI is a major key to developing ethical artificial intelligence.

Liability: Who is responsible for AI?

Last month when an Uber autonomous vehicle killed a pedestrian, it raised many ethical questions. Chief among them is “Who is responsible, and who’s to blame when something goes wrong?” One could blame the developer who wrote the code, the sensor hardware manufacturer, Uber itself, the Uber supervisor sitting in the car, or the pedestrian for crossing outside a crosswalk.

Developing AI will have errors, long-term changes, and unforeseen consequences of the technology. Since AI is so complex, determining liability isn’t trivial. This is especially true when AI has serious implications on human lives, like piloting vehicles, determining prison sentences, or automating university admissions. These decisions will affect real people for the rest of their lives. On one hand, AI may be able to handle these situations more safely and efficiently than humans. On the other hand, it’s unrealistic to expect AI will never make a mistake. Should we write that off as the cost of switching to AI systems, or should we prosecute AI developers when their models inevitably make mistakes?

Security: How do we protect access to AI from bad actors?

As AI becomes more powerful across our society, it will also become more dangerous as a weapon. It’s possible to imagine a scary scenario where a bad actor takes over the AI model that controls a city’s water supply, power grid, or traffic signals. More scary is the militarization of AI, where robots learn to fight and drones can fly themselves into combat.

Cybersecurity will become more important than ever. Controlling access to the power of AI is a huge challenge and a difficult tightrope to walk. We shouldn’t centralise the benefits of AI, but we also don’t want the dangers of AI to spread. This becomes especially challenging in the coming years as AI becomes more intelligent and faster than our brains by an order of magnitude.

Human Interaction: Will we stop talking to one another?

An interesting ethical dilemma of AI is the decline in human interaction. Now more than any time in history it’s possible to entertain yourself at home, alone. Online shopping means you don’t ever have to go out if you don’t want to.

While most of us still have a social life, the amount of in-person interactions we have has diminished. Now, we’re content to maintain relationships via text messages and Facebook posts. In the future, AI could be a better friend to you than your closest friends. It could learn what you like and tell you what you want to hear. Many have worried that this digitization (and perhaps eventual replacement) of human relationships is sacrificing an essential, social part of our humanity.

Employment: Is AI getting rid of jobs?

This is a concern that repeatedly appears in the press. It’s true that AI will be able to do some of today’s jobs better than humans. Inevitably, those people will lose their jobs, and it will take a major societal initiative to retrain those employees for new work. However, it’s likely that AI will replace jobs that were boring, menial, or unfulfilling. Individuals will be able to spend their time on more creative pursuits, and higher-level tasks. While jobs will go away, AI will also create new markets, industries, and jobs for future generations.

Wealth Inequality: Who benefits from AI?

The companies who are spending the most on AI development today are companies that have a lot of money to spend. A major ethical concern is AI will only serve to centralizecoro wealth further. If an employer can lay off workers and replace them with unpaid AI, then it can generate the same amount of profit without the need to pay for employees.

Machines will create wealth more than ever in the economy of the future. Governments and corporations should start thinking now about how we redistribute that wealth so that everyone can participate in the AI-powered economy.

Power & Control: Who decides how to deploy AI?

Along with the centralization of wealth comes the centralization of power and control. The companies that control AI will have tremendous influence over how our society thinks and acts each day. Regulating the development and operation of AI applications will be critical for governments and consumers. Just as we’ve recently seen Facebook get in trouble for the influence its technology and advertising has had on society, we might also see AI regulations that codify equal opportunity for everyone and consumer data privacy.

Robot Rights: Can AI suffer?

A more conceptual ethical concern is whether AI can or should have rights. As a piece of computer code, it’s tempting to think that artificially intelligent systems can’t have feelings. You can get angry with Siri or Alexa without hurting their feelings. However, it’s clear that consciousness and intelligence operate on a system of reward and aversion. As artificially intelligent machines become smarter than us, we’ll want them to be our partners, not our enemies. Codifying humane treatment of machines could play a big role in that.

Ethics in AI in the coming years

Artificial intelligence is one of the most promising technological innovations in human history. It could help us solve a myriad of technical, economic, and societal problems. However, it will also come with serious drawbacks and ethical challenges. It’s important that experts and consumers alike be mindful of these questions, as they’ll determine the success and fairness of AI over the coming years.

By: By Steve Kilpatrick
Co-Founder & Director
Artificial Intelligence & Machine Learning

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