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Category Archives: Machine Learning

AI needs to face up to its invisible-worker problem – MIT Technology Review

But there are a number of problems. One is that workers on these platforms earn very low wages. We did a study where we followed hundreds of Amazon Mechanical Turk workers for several years, and we found that they were earning around $2 per hour. This is much less than the US minimum wage. There are people who dedicate their lives to these platforms; its their main source of income.

And that brings other problems. These platforms cut off future job opportunities as well, because full-time crowdworkers are not given a way to develop their skillsat least not ones that are recognized. We found that a lot of people dont put their work on these platforms on their rsum. If they say they worked on Amazon Mechanical Turk, most employers wont even know what that is. Most employers are not aware that these are the workers behind our AI.

Its clear you have a real passion for what you do. How did you end up working on this?

I worked on a research project at Stanford where I was basically a crowdworker, and it exposed me to the problems. I helped design a new platform, which was like Amazon Mechanical Turk but controlled by the workers. But I was also a tech worker at Microsoft. And that also opened my eyes to what its like working within a large tech company. You become faceless, which is very similar to what crowdworkers experience. And that really sparked me into wanting to change the workplace.

You mentioned doing a study. How do you find out what these workers are doing and what conditions they face?

I do three things. I interview workers, I conduct surveys, and I build tools that give me a more quantitative perspective on what is happening on these platforms. I have been able to measure how much time workers invest in completing tasks. Im also measuring the amount of unpaid labor that workers do, such as searching for tasks or communicating with an employerthings youd be paid for if you had a salary.

Youve been invited to give a talk at NeurIPS this week. Why is this something that the AI community needs to hear?

Well, theyre powering their research with the labor of these workers. I think its very important to realize that a self-driving car or whatever exists because of people that arent paid minimum wage. While were thinking about the future of AI, we should think about the future of work. Its helpful to be reminded that these workers are humans.

Are you saying companies or researchers are deliberately underpaying?

No, thats not it. I think they might underestimate what theyre asking workers to do and how long it will take. But a lot of the time they simply havent thought about the other side of the transaction at all.

Because they just see a platform on the internet. And its cheap.

Yes, exactly.

What do we do about it?

Lots of things. Im helping workers get an idea how long a task might take them to do. This way they can evaluate if a task is going to be worth it. So Ive been developing an AI plug-in for these platforms that helps workers share information and coach each other about which tasks are worth their time and which let you develop certain skills. The AI learns what type of advice is most effective. It takes in the text comments that workers write to each other and learns what advice leads to better results, and promotes it on the platform.

Lets say workers want to increase their wages. The AI identifies what type of advice or strategy is best suited to help workers do that. For instance, it might suggest that you do these types of task from these employers but not these other types of task over there. Or it will tell you not to spend more than five minutes searching for work. The machine-learning model is based on the subjective opinion of workers on Amazon Mechanical Turk, but I found that it could still increase workers wages and develop their skills.

So its about helping workers get the most out of these platforms?

Thats a start. But it would be interesting to think about career ladders. For instance, we could guide workers to do a number of different tasks that let them develop their skills. We can also think about providing other opportunities. Companies putting jobs on these platforms could offer online micro-internships for the workers.

And we should support entrepreneurs. I've been developing tools that help people create their own gig marketplaces. Think about these workers: they are very familiar with gig work and they might have new ideas about how to run a platform. The problem is that they dont have the technical skills to set one up, so Im building a tool that makes setting up a platform a little like configuring a website template.

A lot of this is about using technology to shift the balance of power.

Its about changing the narrative, too. I recently met with two crowdworkers that Ive been talking to and they actually call themselves tech workers, whichI mean, they are tech workers in a certain way because they are powering our tech. When we talk about crowdworkers they are typically presented as having these horrible jobs. But it can be helpful to change the way we think about who these people are. Its just another tech job.

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AI needs to face up to its invisible-worker problem - MIT Technology Review

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AutoML is the Future of Machine Learning – Analytics Insight

AutoML (automated machine learning) is an active area of research in academia and the industry. The cloud vendors promote some or the other form of AutoML services. Likewise, Tech unicorns also offer various AutoML services for its platform users. Additionally, many different open source projects are available, offering exciting new approaches.

The growing desire to gain business value from artificial intelligence (AI) has created a gap between the demand for data science expertise and the supply of data scientist. Running AI and AutoML on the latest Intel architecture addresses this challenge by automating many tasks required to develop AI and machine learning applications.

Using AutoML, businesses can automate tedious and time-consuming manual work required by todays data science. With AutoML, data-savvy users of all levels have access to powerful machine learning algorithms to avoid human error.

With better access to the power of ML, businesses can generate advanced machine learning models without the requirement to understand complex algorithms. Data scientists can apply their specialisation to fine-tune ML models for purposes ranging from manufacturing to retailing to healthcare, and more.

With AutoML, the productivity of repetitive tasks can be increased as it enables a data scientist to focus more on the problem rather than the models. Automating ML pipeline also helps to avoid errors that might creep in manually. AutoML is a step towards democratizing ML by making the power of ML accessible to everybody.

Enterprises seek to automate machine learning pipelines and different steps in the ML workflow to address the increase in tendency and requirement for speeding up AI adoption.

Not everything but many things can be automated in the data science workflow. The pre-implemented model types and structures can be presented or learnt from the input datasets for selection. AutoML simplifies the development of projects, proof of value initiatives, and help business users to stimulate ML solutions development without extensive programming knowledge. It can serve as a complementary tool for data scientists that help them to either quickly find out what algorithms they could try or see if they have skipped some algorithms, and that could have been a valuable selection to obtain better outcomes.

Here are some reasons why business leaders should hire data scientists if they have AutoML tools on their hands:

Essentially, the purpose of AutoML is to automate the repetitive tasks like pipeline creation and hyperparameter tuning so data scientists can spend time on the business problem at hand.

AutoML aims to make the technology available to everyone rather a select few. AutoML and data scientists can work in conjunction to speed up the machine learning process to utilise the real effectiveness of ML.

Whether or not AutoML becomes a success depends mainly on its adoption and the advancements that are made in this sector. However, AutoML is a big part of the future of machine learning.

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AutoML is the Future of Machine Learning - Analytics Insight

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Before machine learning can become ubiquitous, here are four things we need to do now – SiliconANGLE News

It wasnt too long ago that concepts such as communicating with your friends in real time through text or accessing your bank account information all from a mobile device seemed outside the realm of possibility. Today, thanks in large part to the cloud, these actions are so commonplace, we hardly even think about these incredible processes.

Now, as we enter the golden age of machine learning, we can expect a similar boom of benefits that previously seemed impossible.

Machine learning is already helping companies make better and faster decisions. In healthcare, the use of predictive models created with machine learning is accelerating research and discovery of new drugs and treatment regiments. In other industries, its helping remote villages of Southeast Africa gain access to financial services and matching individuals experiencing homelessness with housing.

In the short term, were encouraged by the applications of machine learning already benefiting our world. But it has the potential to have an even greater impact on our society. In the future, machine learning will be intertwined and under the hood of almost every application, business process and end-user experience.

However, before this technology becomes so ubiquitous that its almost boring, there are four key barriers to adoption we need to clear first:

The only way that machine learning will truly scale is if we as an industry make it easier for everyone regardless of skill level or resources to be able to incorporate this sophisticated technology into applications and business processes.

To achieve this, companies should take advantage of tools that have intelligence directly built into applications from which their entire organization can benefit. For example, Kabbage Inc., a data and technology company providing small business cash flow solutions, used artificial intelligence to adapt and help processquickly an unprecedented number of small business loans and unemployment claims caused by COVID-19 while preserving more than 945,000 jobs in America. By folding artificial intelligence into personalization, document processing, enterprise search, contact center intelligence, supply chain or fraud detection, all workers can benefit from machine learning in a frictionless way.

As processes go from manual to automatic, workers are free to innovate and invent, and companies are empowered to be proactive instead of reactive. And as this technology becomes more intuitive and accessible, it can be applied to nearly every problem imaginable from the toughest challenges in the information technology department to the biggest environmental issues in the world.

According to the World Economic Forum, the growth of AI could create 58 million net new jobs in the next few years. However, research suggests that there are currently only 300,000 AI engineers worldwide, and AI-related job postings are three times that of job searches with a widening divergence.

Given this significant gap, organizations need to recognize that they simply arent going to be able to hire all the data scientists they need as they continue to implement machine learning into their work. Moreover, this pace of innovation will open doors and ultimately create jobs we cant even begin to imagine today.

Thats why companies around the world such asMorningstar, Liberty MutualandDBS Bank are finding innovative ways to encourage their employees to gain new machine learning skills with a fun, interactive hands-on approach. Its critical that organizations should not only direct their efforts towards training the workforce they have with machine learning skills, but also invest in training programs that develop these important skills in the workforce of tomorrow.

With anything new, often people are of two minds: Either an emerging technology is a panacea and global savior, or it is a destructive force with cataclysmic tendencies. The reality is, more often than not, a nuance somewhere in the middle. These disparate perspectives can be reconciled with information, transparency and trust.

As a first step, leaders in the industry need to help companies and communities learn about machine learning, how it works, where it can be applied and ways to use it responsibly, and understand what it is not.

Second, in order to gain faith in machine learning products, they need to be built by diverse groups of people across gender, race, age, national origin, sexual orientation, disability, culture and education. We will all benefit from individuals who bring varying backgrounds, ideas and points of view to inventing new machine learning products.

Third, machine learning services should be rigorously tested, measuring accuracy against third party benchmarks. Benchmarks should be established by academia, as well as governments, and be applied to any machine learning-based service, creating a rubric for reliable results, as well as contextualizing results for use cases.

Finally, as a society, we need to agree on what parameters should be put in place governing how and when machine learning can be used. With any new technology, there has to be a balance in protecting civil rights while also allowing for continued innovation and practical application of the technology.

Any organization working with machine learning technology should be engaging customers, researchers, academics and others to determine the benefits of its machine learning technology along with the potential risks. And they should be in active conversation with policymakers, supporting legislation, and creating their own guidelines for the responsible use of machine learning technology. Transparency, open dialogue and constant evaluation must always be prioritized to ensure that machine learning is applied appropriately and is continuously enhanced.

Through machine learning weve already accomplished so much, and yet its still day one (and we havent even had a cup of coffee yet!). If were using machine learning to help endangered orangutans, just imagine how it could be used to help save and preserve our oceans and marine life. If were using this technology to create digital snapshots of the planets forests in real-time, imagine how it could be used to predict and prevent forest fires. If machine learning can be used to help connect small-holding farmers to the people and resources they need to achieve their economic potential, imagine how it could help end world hunger.

To achieve this reality, we as an industry have a lot of work ahead of us. Im incredibly optimistic that machine learning will help us solve some of the worlds toughest challenges and create amazing end-user experiences weve never even dreamed. Before we know it, machine learning will be as familiar as reaching for our phones.

Swami Sivasubramanianis vice president of Amazon AI, running AI and machine learning services for Amazon Web Services Inc. He wrote this article for SiliconANGLE.

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Before machine learning can become ubiquitous, here are four things we need to do now - SiliconANGLE News

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Commentary: Pathmind applies AI, machine learning to industrial operations – FreightWaves

The views expressed here are solely those of the author and do not necessarily represent the views of FreightWaves or its affiliates.

In this installment of the AI in Supply Chain series (#AIinSupplyChain), we explore how Pathmind, an early-stage startup based in San Francisco, is helping companies apply simulation and reinforcement learning to industrial operations.

I asked Chris Nicholson, CEO and founder of Pathmind, What is the problem that Pathmind solves for its customers? Who is the typical customer?

Nicholson said: The typical Pathmind customer is an industrial engineer working at a simulation consulting firm or on the simulation team of a large corporation with industrial operations to optimize. This ranges from manufacturing companies to the natural resources sector, such as mining and oil and gas. Our clients build simulations of physical systems for routing, job scheduling or price forecasting, and then search for strategies to get more efficient.

Pathminds software is suited for manufacturing resource management, energy usage management optimization and logistics optimization.

As with every other startup that I have highlighted as a case in this #AIinSupplyChain series, I asked, What is the secret sauce that makes Pathmind successful? What is unique about your approach? Deep learning seems to be all the rage these days. Does Pathmind use a form of deep learning? Reinforcement learning?

Nicholson responded: We automate tasks that our users find tedious or frustrating so that they can focus on whats interesting. For example, we set up and maintain a distributed computing cluster for training algorithms. We automatically select and tune the right reinforcement learning algorithms, so that our users can focus on building the right simulations and coaching their AI agents.

Echoing topics that we have discussed in earlier articles in this series, he continued: Pathmind uses some of the latest deep reinforcement learning algorithms from OpenAI and DeepMind to find new optimization strategies for our users. Deep reinforcement learning has achieved breakthroughs in gaming, and it is beginning to show the same performance for industrial operations and supply chain.

On its website, Pathmind describes saving a large metals processor 10% of its expenditures on power. It also describes the use of its software to increase ore preparation by 19% at an open-pit mining site.

Given how difficult it is to obtain good quality data for AI and machine learning systems for industrial settings, I asked how Pathmind handles that problem.

Simulations generate synthetic data, and lots of it, said Slin Lee, Pathminds head of engineering. The challenge is to build a simulation that reflects your underlying operations, but there are many tools to validate results.

Once you pass the simulation stage, you can integrate your reinforcement learning policy into an ERP. Most companies have a lot of the data they need in those systems. And yes, theres always data cleansing to do, he added.

As the customer success examples Pathmind provides on its website suggest, mining companies are increasingly looking to adopt and implement new software to increase efficiencies in their internal operations. This is happening because the industry as a whole runs on very old technology, and deposits of ore are becoming increasingly difficult to access as existing mines reach maturity. Moreover, the growing trend toward the decarbonization of supply chains, and the regulations that will eventually follow to make decarbonization a requirement, provide an incentive for mining companies to seize the initiative in figuring out how to achieve that goal by implementing new technology

The areas in which AI and machine learning are making the greatest inroads are mineral exploration using geological data to make the process of seeking new mineral deposits less prone to error and waste; predictive maintenance and safety using data to preemptively repair expensive machinery before breakdowns occur; cyberphysical systems creating digital models of the mining operation in order to quickly simulate various scenarios; and autonomous vehicles using autonomous trucks and other autonomous vehicles and machinery to move resources within the area in which mining operations are taking place.

According to Statista, The revenue of the top 40 global mining companies, which represent a vast majority of the whole industry, amounted to some 692 billion U.S. dollars in 2019. The net profit margin of the mining industry decreased from 25 percent in 2010 to nine percent in 2019.

The trend toward mining companies and other natural-resource-intensive industries adopting new technology is going to continue. So this is a topic we will continue to pay attention to in this column.

Conclusion

If you are a team working on innovations that you believe have the potential to significantly refashion global supply chains, wed love to tell your story at FreightWaves. I am easy to reach on LinkedIn and Twitter. Alternatively, you can reach out to any member of the editorial team at FreightWaves at media@freightwaves.com.

Dig deeper into the #AIinSupplyChain Series with FreightWaves:

Commentary: Optimal Dynamics the decision layer of logistics? (July 7)

Commentary: Combine optimization, machine learning and simulation to move freight (July 17)

Commentary: SmartHop brings AI to owner-operators and brokers (July 22)

Commentary: Optimizing a truck fleet using artificial intelligence (July 28)

Commentary: FleetOps tries to solve data fragmentation issues in trucking (Aug. 5)

Commentary: Bulgarias Transmetrics uses augmented intelligence to help customers (Aug. 11)

Commentary: Applying AI to decision-making in shipping and commodities markets (Aug. 27)

Commentary: The enabling technologies for the factories of the future (Sept. 3)

Commentary: The enabling technologies for the networks of the future (Sept. 10)

Commentary: Understanding the data issues that slow adoption of industrial AI (Sept. 16)

Commentary: How AI and machine learning improve supply chain visibility, shipping insurance (Sept. 24)

Commentary: How AI, machine learning are streamlining workflows in freight forwarding, customs brokerage (Oct. 1)

Commentary: Can AI and machine learning improve the economy? (Oct. 8)

Commentary: Savitude and StyleSage leverage AI, machine learning in fashion retail (Oct. 15)

Commentary: How Japans ABEJA helps large companies operationalize AI, machine learning (Oct. 26)

Authors disclosure: I am not an investor in any early-stage startups mentioned in this article, either personally or through REFASHIOND Ventures. I have no other financial relationship with any entities mentioned in this article.

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Artificial Intelligence and Machine Learning, 5G and IoT will be the Most Important Technologies in 2021, According to new IEEE Study – PRNewswire

PISCATAWAY, N.J., Nov. 19, 2020 /PRNewswire/ --IEEE, the world's largest technical professional organization dedicated to advancing technology for humanity, today released the results of a survey of Chief Information Officers (CIOs) and Chief Technology Officers (CTOs) in the U.S., U.K., China, India and Brazil regarding the most important technologies for 2021 overall, the impact of the COVID-19 pandemic on the speed of their technology adoption and the industries expected to be most impacted by technology in the year ahead.

2021 Most Important Technologies and ChallengesWhich will be the most important technologies in 2021? Among total respondents, nearly one-third (32%) say AI and machine learning, followed by 5G (20%) and IoT (14%).

Manufacturing (19%), healthcare (18%), financial services (15%) and education (13%) are the industries that most believe will be impacted by technology in 2021, according to CIOs and CTOS surveyed. At the same time, more than half (52%) of CIOs and CTOs see their biggest challenge in 2021 as dealing with aspects of COVID-19 recovery in relation to business operations. These challenges include a permanent hybrid remote and office work structure (22%), office and facilities reopenings and return (17%), and managing permanent remote working (13%). However, 11% said the agility to stop and start IT initiatives as this unpredictable environment continues will be their biggest challenge. Another 11% cited online security threats, including those related to remote workers, as the biggest challenge they see in 2021.

Technology Adoption, Acceleration and Disaster Preparedness due to COVID-19CIOs and CTOs surveyed have sped up adopting some technologies due to the pandemic:

The adoption of IoT (42%), augmented and virtual reality (35%) and video conferencing (35%) technologies have also been accelerated due to the global pandemic.

Compared to a year ago, CIOs and CTOs overwhelmingly (92%) believe their company is better prepared to respond to a potentially catastrophic interruption such as a data breach or natural disaster. What's more, of those who say they are better prepared, 58% strongly agree that COVID-19 accelerated their preparedness.

When asked which technologies will have the greatest impact on global COVID-19 recovery, one in four (25%) of those surveyed said AI and machine learning,

CybersecurityThe top two concerns for CIOs and CTOs when it comes to the cybersecurity of their organization are security issues related to the mobile workforce including employees bringing their own devices to work (37%) and ensuring the Internet of Things (IoT) is secure (35%). This is not surprising, since the number of connected devices such as smartphones, tablets, sensors, robots and drones is increasing dramatically.

Slightly more than one-third (34%) of CIO and CTO respondents said they can track and manage 26-50% of devices connected to their business, while 20% of those surveyed said they could track and manage 51-75% of connected devices.

About the Survey"The IEEE 2020 Global Survey of CIOs and CTOs" surveyed 350 CIOs or CTOs in the U.S., China, U.K., India and Brazil from September 21 - October 9, 2020.

About IEEEIEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity. Through its highly cited publications, conferences, technology standards, and professional and educational activities, IEEE is the trusted voice in a wide variety of areas ranging from aerospace systems, computers, and telecommunications to biomedical engineering, electric power, and consumer electronics

SOURCE IEEE

https://www.ieee.org

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Artificial Intelligence and Machine Learning, 5G and IoT will be the Most Important Technologies in 2021, According to new IEEE Study - PRNewswire

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DIY Camera Uses Machine Learning to Audibly Tell You What it Sees – PetaPixel

Adafruit Industries has created a machine learning camera built with the Raspberry Pi that can identify objects extremely quickly and audibly tell you what it sees. The group has listed all the necessary parts you need to build the device at home.

The camera is based on Adafruits BrainCraft HAT add-on for the Raspberry Pi 4, and uses TensorFlow Lite object recognition software to be able to recognize what it is seeing. According to Adafruits website, its compatible with both the 8-megapixel Pi camera and the 12.3-megapixel interchangeable lens version of module.

While interesting on its own, DIY Photography makes a solid point by explaining a more practical use case for photographers:

You could connect a DSLR or mirrorless camera from its trigger port into the Pis GPIO pins, or even use a USB connection with something like gPhoto, to have it shoot a photo or start recording video when it detects a specific thing enter the frame.

A camera that is capable of recognizing what it is looking at could be used to only take a photo when a specific object, animal, or even a person comes into the frame. That would mean it could have security system or wildlife monitoring applications. Whenever you might wish your camera knew what it was looking at, this kind of technology would make that a reality.

You can find all the parts you will need to build your own version of this device on Adafruits website here. They also have published an easy machine learning guide for the Raspberry Pi as well as a guide on running TensorFlow Lite.

(via DPReview and DIY Photography)

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