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Adi Singh, Product Manager in Robotics at Canonical – Interview Series

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Adi Singh, is the Product Manager in Robotics at Canonical.   Canonical specializes in open source software, including Ubuntu, the world’s most popular enterprise Linux from cloud to edge, and they have a global community of 200,000 contributors.

Ubuntu is the most popular Linux distribution for large embedded systems. As autonomous robots mature, innovative tech companies turn to Ubuntu, we discuss advantages of building a robot using open source software and other key considerations.

What sparked your initial interest in robotics?

A few years into software programming, I was dissatisfied with seeing my work only running on a screen. I had an urge to see some physical action, some tangible response, some real-world result of my engineering. Robotics was a natural answer to this urge.

Can you describe your day to day role with Canonical?

I define and lead the product strategy for Robotics and Automotive verticals at Canonical. I am responsible for coordinating product development, executing go-to-market strategies, and engagements with external organizations related to my domain.

Why is building a robot on open source software so important?

Building anything on open source software is usually a wise idea as it allows you to stand on the shoulders of giants. Individuals and companies alike benefit from the volunteer contributions of some of the brightest minds in the world when they decide to build on a foundation of open source software. As a result, popular FOSS repositories are very robustly engineered and very actively maintained; allowing users to focus on their innovation rather than the nuts and bolts of every library going into their product.

Can you describe what the Ubuntu open source platform offers to IoT and robotics developers?

Ubuntu is the platform of choice for developers around the world for frictionless IoT and robotics development. A number of popular frameworks that help with device engineering are built on Ubuntu, so the OS is able to provide several tools for building and deploying products in this area right out of the box. For instance, the most widely used middleware for robotics development – ROS – is almost entirely run on Ubuntu distros (More than 99.5% according to official metrics here: https://metrics.ros.org/packages_linux.html).

What are some of the key considerations that should be analyzed when choosing a robot’s operating system?

Choosing the right operating system is one of the most important decisions to be made when building a new robot, including several development factors. Hardware and software stack compatibility is key as ample time will be spent ensuring components will work well together so as to not hinder progress on developing the robot itself.

Also, prior familiarity of the operating systems by the dev team is a huge factor affecting economics, as previous experience will no doubt help to accelerate the overall robot development process and thereby cut down on the time to market. Ease of system integration and third-party add-ons should also be heavily considered. A robot is rarely a standalone device and often needs to seamlessly interact with other devices. These companion devices may be as simple as a digital twin for hardware-in-the-loop testing, but in general, off-device computation is getting more popular in robotics. Cloud robotics, speech processing and machine learning are all use-cases that can benefit from processing information in a server farm instead of on a resource-constrained robot.

Additionally, robustness and a level of security engineered into the kernel is imperative. Availability of long-term support for the operating system, especially from the community, is another factor. Something to keep in mind is that operating systems are typically only supported for a set amount of time. For example, long-term support (LTS) releases of Android Things are supported for three years, whereas Ubuntu and Ubuntu Core are supported for five years (or for 10 years with Extended Security Maintenance). If the supported lifespan of the operating system is shorter than the anticipated lifespan of the robot in the field, it will eventually stop getting updates and die early.

Thank for for the interview, readers who wish to learn more should visit Ubuntu Robotics.

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Antoine Tardif is a Futurist who is passionate about the future of AI and robotics. He is the CEO of BlockVentures.com, and has invested in over 50 AI & blockchain projects. He is also the Co-Founder of Securities.io a news website focusing on digital securities, and is a founding partner of unite.ai

Ethics

Andrea Sommer, Founder & Business Lead at UvvaLabs – Interview Series

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Andrea Sommer is the Founder & Business Lead at UvvaLabs, a female-founded technology company that uses AI to help companies make better decisions that create more diverse and accessible workforces.

Could you discuss how UvvaLabs uses AI to assist companies in creating more diverse and accessible workforces?

Our approach looks at offering structural solutions to the very structural problem of inequity in the workplace. Through our research and experience, we’ve built a model of what the ‘ideal’ organization looks like from a diversity and accessibility perspective. Our AI analyzes and evaluates data across an organization to create a version of that organization’s ‘current state’ from a diversity perspective. By comparing the two sides – the ideal to the current – we can offer recommendations on what structures to build and which to remove to bring the organization closer to that ideal state.

What was the inspiration for launching UvvaLabs?

My co-founder and I are childhood friends who have had a lifelong passion for dismantling the barriers to equity, but we’ve done so in very different ways. My co-founder Laura took the academic path, getting a PhD in Sociology from UC Berkeley. Her research and experience has been focused on building rigorous methodologies that work in low-quality data environments, especially studying racial bias. I went down the business path, first working as a strategist across global technology brands, getting an MBA from London Business School and then building my first business in the analytics space. Despite our divergent paths we have stayed in touch throughout the years. When I returned to the US after living in London for the last 11 years, the opportunity to collaborate on a project together presented itself and UvvaLabs was born.

One current issue with using AI to hire staff is that it can unintentionally reinforce societal biases such as racism and sexism. How big of an issue do you believe this to be?

This is a huge issue. Frequently decision makers believe that AI can solve all problems instead of understanding that it is a tool that requires a human counterpart to make smart decisions. Recruitment is no different – there are many products out there that claim to reduce or remove bias from the process. But AI is only as strong as the algorithm running it, and this is always built by people. Even the strongest AI system cannot be completely free of bias since all humans have biases.

For example, many AI recruitment tools are designed to offer or match candidates to a role in the most cost-effective way possible. This unintended focus on cost actually creates a huge inflection point for bias. In typical organizations, hiring diverse talent takes more time and effort because power structures tend to reproduce themselves and tend to be homogenous. However, the benefits of building a more diverse workforce far outweigh any initial costs.

How does UvvaLabs avoid having these biases into the AI system?

The best way to build any technology including AI that is free from bias is by having a team that is composed of both people who have been historically marginalized and who are experts in research methods designed to minimize bias. That’s the approach we take at UvvaLabs.

Uvvalabs uses a broad variety of data sources to understand an organization’s diversity environment. Could you touch on what some of these data sources are?

Organizations are low-quality data environments. Frequently there is little consistency between companies or even departments in terms of what is created and how. Our technology is designed to provide rigorous analysis in these types of environments by combining a mixture of quantitative and qualitative data sources. The key for us is that we only analyze what is readily available and easily shareable – so that the approach is as low-touch as possible.

Uvvalabs offers a dashboard showing various indicators for organizational health. Could you discuss what these indicators are and the type of actionable insight that is provided? 

Every organization is different, so each organization will likely use Uvva in a slightly different way. This is because every organization is at a different stage in their diversity journey. There is no one size fits all formula – our approach flexes to each organization’s priorities, what is currently being measured and available, as well as where the organization wants to go. This exercise is what defines the recommendations our tool provides.

As a woman serial entrepreneur do you have any advice for women who are contemplating launching a new business?

Startups are a boy’s club and it is objectively harder for women, and even harder for women of color. We shouldn’t shy away from the reality that women and people of color have been systematically shut out of opportunities, capital, communities and networks of access. That said, this is slowly changing. For instance, more and more funds are opening up that specifically are geared towards women or BIPOC. Incubators and accelerators are thinking and acting more inclusively as they shape their programs and practices. Diverse entrepreneurial communities are emerging and growing.

My advice for anyone who aspires to be an entrepreneur is to take a stab. It won’t always be easy. And it might not work. But entrepreneurship is filled with people who break with convention and prove naysayers wrong. We need more women and minorities in this community. We need their dreams, their products and their stories.

You are also the founder of Hive Founders, a non-profit network that brings female founders together. Could you give us some details on this non-profit and how it can help women?

Hive Founders is a global network of support for women across the globe, no matter what stage they are in. Every business is unique but there are many lessons we can learn from each other. In addition to the community, Hive Founders hosts events, podcasts, and a newsletter – all designed to bring resources and knowledge to our community of founders.

Is there anything else that you would like to share about UvvaLabs?

Every organization has the potential to transform itself into a more productive, diverse and accessible workplace, regardless of what structures are in place today. There are competitive reasons for investing in diversity. For one, the customer landscape is changing – the United States for instance will be majority minority by 2044. In practice this means customer profiles are changing too. Every company wants to be as attractive as possible to their customers and as competitive as possible against similar offerings. Diversity is that competitive asset. Smart companies and their leaders understand this and will get ahead of the curve to ensure their workplaces and products serve and support as many different types of people as possible.

Thank you for the great interview, I really enjoyed learning about your views on diversity and AI bias. Readers who wish to learn more should visit UvvaLabs.

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Data Science

Jean Belanger, Co-Founder & CEO at Cerebri AI – Interview Series

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Jean Belanger, is the Co-Founder & CEO at Cerebri AI, a pioneer in artificial intelligence and machine learning, is the creator of Cerebri Values™, the industry’s first universal measure of customer success. Cerebri Values quantifies each customer’s commitment to a brand or product and dynamically predicts “Next Best Actions” at scale, which enables large companies to focus on accelerating profitable growth.

What was it that initially attracted you to AI?

Cerebri AI is my 2nd data science startup. My first used operations research modelling to optimize order processing for major retail and ecommerce operations. 4 of the top US 10 retailers, including Walmart, used our technology. AI has a huge advantage, which really attracted me. Models learn, which means they are more scalable. Which means we can build and scale awesome technology that really, really adds value.

Can you tell us about your journey to become a co-founder of Cerebri AI?

I was mentoring at a large accelerator here in Austin, Texas – Capital Factory – and I was asked to write the business plan for Cerebri AI.  So, I leveraged my experience of doing data science, with over 80 data science-based installs using our technology. Sometimes you just need to go for it.

What are some of the challenges that enterprises currently face when it comes to CX and customer/brand relationships?

The simple answer is that every business tries to understand their customers’ behavior, so they can satisfy their needs. You cannot get into someone’s head to sort out why they buy a product or service when they do, so brands must do the best they can. Surveys, tracking market share, or measuring market segmentation. There are thousands of ways of tracking or understanding customers. However, the underlying basis for everything is rarely thought about, and that is Moore’s Law.  More powerful, cheaper semiconductors, processors etc., from Intel, Apple, Taiwan Semi, etc., make our modern economy work at such a compute intense level relative to a few years ago. Today, the cost of cloud computing and memory resources make AI doable.  AI is VERY compute intensive. Things that were not possible, even five years ago, can now be done. In terms of customer behavior, we can now process all the info and data that we have digitally recorded in one customer journey per customer. So, customer behavior is suddenly much easier to understand and react to. This is key, and that is the future of selling products and services.

Cerebri AI personalizes the enterprise by combining machine learning and cloud computing to enhance brand commitment. How does the AI increase brand commitment?

When Cerebri AI looks at a customer, the first thing we establish is their commitment to the brand we are working with. We define commitment to the brand as the customer’s willingness to spend in the future. Its fine to be in business and have committed customers, but if they do not buy your goods and services, then in effect, you are out of business. The old saying goes – if you cannot measure something, you cannot improve it.  Now we can measure commitment and other key metrics, which means we can use our data monitoring tools and study a customer’s journey to see what works and what does not. Once we find a tactic that works, our campaign building tools can instantly build a cohort of customers that might be similarly impacted. All of this is impossible without AI and the cloud infrastructure at the software layer, which allows us to move in so many directions with customers.

What type of data does Cerebri collect? Or use within its system? How does this comply with PII (Personally Identifiable Information) restrictions?

Until now we only operate behind the customer’s firewall, so PII has not been an issue. We are going to open a direct access web site in the Fall, so that will require use of anonymized data. We are excited about the prospect of bringing our advanced technology to a broader array of companies and organizations.

You are working with the Bank of Canada, Canada’s central bank, to introduce AI to their macroeconomic forecasting. Could you describe this relationship, and how your platform is being used?

The Bank of Canada is an awesome customer. Brilliant people and macroeconomic experts.  We started 18 months or so ago. Introducing AI into the technology choices the bank’s team would have at their disposal. We started with predictions of quarterly GDP for Canada.  That was great, now we are expanding the dataset used in the AI-based forecasts to increase accuracy, etc.  To do this, we developed an AI optimizer, which automates the thousands of choices facing a data scientist when they carry out a modelling exercise. Macro-economic time series require a very sophisticated approach when you are dealing with decades of data, all of which may have an impact on overall GDP.  The AI Optimizer was so successful that we decided to incorporate this into Cerebri AI’s standard CCX platform offering.  It will be used in all future engagements.  Amazing technology.  One of the reasons we have filed 24 patents to date.

Cerebri AI launched CCX v2 in the autumn last year. What is this platform exactly?

Our CCX offering has three components.

Our CCX platform, which consists of a 10-stage software pipeline, which our data scientists use to build their models and product insights. It is also our deployment system from data intake to our UX and insights.  We have several applications in our offering, such as QM for quality management of the entire process, and Audit, which tells users what features drive the insights they are seeing.

Then, we have our Insights themselves, which are generated from our modelling technology. Our flagship insight is our Cerebri Values, which is a customer’s commitment to your brand, which is – in effect – a measure of how much money a customer is willing to spend in the future on a brand’s products and services.

We derive a host of customer engagement and revenue KPI insights from our core offering and we can help with our next best action{set}s to drive engagement, up-selling, cross-selling, reducing churn, etc.

You sat down to interview representatives from four major faith traditions in the world today — Islam, Hinduism, Judaism and Christianity. Have your views of the world shifted since these interviews, and is there one major insight that you would like to share with our readers during the current pandemic?

Diversity matters. Not because it is a goal in and of itself, but because treating anyone in anything less than a totally equitable manner is just plain stupid. Period. When I was challenged to put in a program to reinforce Cerebri AI’s commitment to diversity, it was apparent to me that what we used to learn as children, in our houses of worship, has been largely forgotten.  So, I decided to ask the faith communities and their leaders in the US to tell us how they think through treating everyone equally. The sessions have proved to be incredibly popular, and we make them available to anyone who wants to use them in their business.

On the pandemic, I have an expert at home. My wife is a world-class epidemiologist.  She told me on day one. Make sure the people most at risk are properly isolated, she called this epi-101. This did not happen. The effects have been devastating.  Age discrimination is not just an equity problem in working, it is also all about how we treat our parents, grandparents, etc., wherever they are residing.  We did not distinguish ourselves in the pandemic in how we dealt with nursing home residents, for example, a total disaster in many communities. I live in Texas, we are the 2nd biggest state population wise, and our pandemic-related deaths per population is 40th in the US among all states.  Arguably the best in Europe is Germany with 107 pandemic deaths per million, Texas sits at 77, so our state authorities have done a great job so far.

You’ve stated that a lot of the media focuses on the doom and gloom of AI but does not focus enough on how the technology can be useful to make our lives better. What are your views on some of the improvements in our lives that we will witness from the further advancement of AI?

Our product helps eliminate spam email from the vendors you do business with. Does it get better than that? Just kidding. There are so many fields where AI is helping, it is difficult to imagine a world without AI.

Is there anything else that you would like to share about Cerebri AI?

The sky’s the limit, as understanding customer behavior is only really just beginning. Being enabled for the first time by AI and the totally massive compute power available on the cloud and due to Moore’s Law.

Thank you for the great interviews, readers who wish to learn more should visit Cerebri AI.

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Computing

Huma Abidi, Senior Director of AI Software Products at Intel – Interview Series

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Photo By O’Reilly Media

Huma Abidi is a Senior Director of AI Software Products at Intel, responsible for strategy, roadmaps, requirements, machine learning and analytics software products. She leads a globally diverse team of engineers and technologists responsible for delivering world-class products that enable customers to create AI solutions. Huma joined Intel as a software engineer and has since worked in a variety of engineering, validation and management roles in the area of compilers, binary translation, and AI and deep learning. She is passionate about women’s education, supporting several organizations around the world for this cause, and was a finalist for VentureBeat’s 2019 Women in AI award in the mentorship category.

What initially sparked your interest in AI?

I’ve always found it interesting to imagine what could happen if machines could speak, or see, or interact intelligently with humans. Because of some big technical breakthroughs in the last decade, including deep learning gaining popularity because of the availability of data, compute power, and algorithms, AI has now moved from science fiction to real world applications. Solutions we had imagined previously are now within reach. It is truly an exciting time!

In my previous job, I was leading a Binary Translation engineering team, focused on optimizing software for Intel hardware platforms. At Intel, we recognized that the developments in AI would lead to huge industry transformations, demanding tremendous growth in compute power from devices to Edge to cloud and we sharpened our focus to become a data-centric company.

Realizing the need for powerful software to make AI a reality, the first challenge I took on was to lead the team in creating AI software to run efficiently on Intel Xeon CPUs by optimizing deep learning frameworks like Caffe and TensorFlow. We were able to demonstrate more than 200-fold performance increases due to a combination of Intel hardware and software innovations.

We are working to make all of our customer workloads in various domains run faster and better on Intel technology.

 

What can we do as a society to attract women to AI?

It’s a priority for me and for Intel to get more women in STEM and computer science in general, because diverse groups will build better products for a diverse population. It’s especially important to get more women and underrepresented minorities in AI, because of potential biases lack of representation can cause when creating AI solutions.

In order to attract women, we need to do a better job explaining to girls and young women how AI is relevant in the world, and how they can be part of creating exciting and impactful solutions. We need to show them that AI spans so many different areas of life, and they can use AI technology in their domain of interest, whether it’s art or robotics or data journalism or television. Exciting applications of AI they can easily see making an impact e.g. virtual assistants like Alexa, self-driving cars, social media, how Netflix knows which movies they want to watch, etc.

Another key part of attracting women is representation. Fortunately, there are many women leaders in AI who can serve as excellent role models, including Fei-Fei Li, who is leading human-centered AI at Stanford, and Meredith Whittaker, who is working on social implications through the AI Now Institute at NYU.

We need to work together to adopt inclusive business practices and expand access of technology skills to women and underrepresented minorities. At Intel, our 2030 goal is to increase women in technical roles to 40% and we can only achieve that by working with other companies, institutes, and communities.

 

How can women best break into the industry?  

There are a few options if you want to break into AI specifically. There are numerous online courses in AI, including UDACITY’s free Intel Edge AI Fundamentals course. Or you could go back to school, for example at one of Maricopa County’s community colleges for an AI associate degree – and study for a career in AI e.g. Data Scientist, Data Engineer, ML/DL developer, SW Engineer etc.

If you already work at a tech company, there are likely already AI teams. You could check out the option to spend part of your time on an AI team that you’re interested in.

You can also work on AI if you don’t work at a tech company. AI is extremely interdisciplinary, so you can apply AI to almost any domain you’re involved in. As AI frameworks and tools evolve and become more user-friendly, it becomes easier to use AI in different settings. Joining online events like Kaggle competitions is a great way to work on real-world machine learning problems that involve data sets you find interesting.

The tech industry also needs to put in time, effort, and money to reach out to and support women, including women who are also underrepresented ethnic minorities. On a personal note, I’m involved in organizations like Girls Who Code and Girl Geek X, which connect and inspire young women.

 

With Deep learning and reinforcement learning recently gaining the most traction, what other forms of machine learning should women pay attention to?

AI and machine learning are still evolving, and exciting new research papers are being published regularly. Some areas to focus on right now include:

  1. Classical ML techniques that continue to be important and are widely used.
  2. Responsible/Explainable AI, that has become a critical part of AI lifecycle and from a deployability of deep learning and reinforcement learning point-of-view.
  3. Graph Neural Networks and multi-modal learning that get insights by learning from rich relation information among graph data.

 

AI bias is a huge societal issue when it comes to bias towards women and minorities. What are some ways of solving these issues?

When it comes to AI, biases in training samples, human labelers and teams can be compounded to discriminate against diverse individuals, with serious consequences.

It is critical that diversity is prioritized at every step of the process. If women and other minorities from the community are part of the teams developing these tools, they will be more aware of what can go wrong.

It is also important to make sure to include leaders across multiple disciplines such as social scientists, doctors, philosophers and human rights experts to help define what is ethical and what is not.

 

Can you explain the AI blackbox problem, and why AI explainability is important?

In AI, models are trained on massive amounts of data before they make decisions. In most AI systems, we don’t know how these decisions were made — the decision-making process is a black box, even to its creators. And it may not be possible to really understand how a trained AI program is arriving at its specific decision. A problem arises when we suspect that the system isn’t working. If we suspect the system of algorithmic biases, it’s difficult to check and correct for them if the system is unable to explain its decision making.

There is currently a major research focus on eXplainable AI (XAI) that intends to equip AI models with transparency, explainability and accountability, which will hopefully lead to Responsible AI.

 

In your keynote address during MITEF Arab Startup Competition final award ceremony and conference you discussed Intel’s AI for Social Good initiatives. Which of these Social Good projects has caught your attention and why is it so important?

I continue to be very excited about all of Intel’s AI for Social Good initiatives, because breakthroughs in AI can lead to transformative changes in the way we tackle problem solving.

One that I especially care about is the Wheelie, an AI-powered wheelchair built in partnership with HOOBOX Robotics. The Wheelie allows extreme paraplegics to regain mobility by using facial expressions to drive. Another amazing initiative is TrailGuard AI, which uses Intel AI technology to fight illegal poaching and protect animals from extinction and species loss.

As part of Intel’s Pandemic Response Initiative, we have many on-going projects with our partners using AI. One key initiative is contactless fever detection or COVID-19 detection via chest radiography with Darwin AI. We’re also working on bots that can answer queries to increase awareness using natural language processing in regional languages.

 

For women who are interested in getting involved, are there books, websites, or other resources that you would recommend?  

There are many great resources online, for all experience levels and areas of interest. Coursera and Udacity offer excellent online courses on machine learning and seep learning, most of which can be audited for free. MIT’s OpenCourseWare is another great, free way to learn from some of the world’s best professors.

Companies such as Intel have AI portals that contain a lot of information about AI including offered solutions. There are many great books on AI: foundational computer science texts like Artificial Intelligence: A Modern Approach by Peter Norvig and Stuart Russell, and modern, philosophical books like Homo Deus by historian Yuval Hararri. I’d also recommend Lex Fridman’s AI podcast on great conversations from a wide range of perspectives and experts from different fields.

 

Do you have any last words for women who are curious about AI but are not yet ready to leap in?

AI is the future, and will change our society — in fact, it already has. It’s essential that we have honest, ethical people working on it. Whether in a technical role, or at a broader social level, now is a perfect time to get involved!

Thank you for the interview, you are certainly an inspiration for women the world over. Readers who wish to learn more about the software solutions at Intel should visit AI Software Products at Intel.

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