Speaker 1: Hello, my name is Graham Class and I'm your host
Speaker 1: for this season of technically speaking, an Intel podcast. While
Speaker 1: Intel is at the forefront of so many cutting edge technologies.
Speaker 1: This season is all about artificial intelligence and that's why
Speaker 1: I've been tapped as your host. Having a background in tech,
Speaker 1: as a software engineer. I was always interested in merging
Speaker 1: the advances of artificial intelligence with my love for media
Speaker 1: is culminated in one of my other projects, Daily Dad jokes,
Speaker 1: an A I powered podcast, churning out jokes and humor
Speaker 1: for listeners worldwide.
Speaker 1: But artificial Intelligence can do a lot more than help
Speaker 1: whip up a corny joke. This technology has been revolutionizing
Speaker 1: the way we engage with the world with innovations across healthcare, agriculture,
Speaker 1: business and even the public sector.
Speaker 1: Another way that artificial intelligence is changing the world is
Speaker 1: through philosophy. The term ethical A I is a framework
Speaker 1: on how to use A I, what system should be
Speaker 1: in place to govern its use with business and consumers.
Speaker 1: In this episode, we'll dive into the ethics of artificial
Speaker 1: intelligence with one of the pioneers in the field.
Speaker 1: Joining me for today's conversation is Intel's Ria Chu R
Speaker 1: A can perhaps be described as the moral compass of
Speaker 1: the company's A I as an A I software architect
Speaker 1: and generative A I evangelist. She is charged with finding
Speaker 1: responsible trustworthy solutions for Intel's Internet of Things Engineering group.
Speaker 1: Her role exists at the intersection of hardware and software
Speaker 1: product design and effective consumer use.
Speaker 1: Having studied extensively at Harvard in the subjects of computer
Speaker 1: science and data science. The domains of expertise are solutions
Speaker 1: for security and privacy in machine learning, fairness, explainable and
Speaker 1: responsible A I systems uncertain A I reinforcement learning and
Speaker 1: computational models of intelligence. She is a reoccurring keynote speaker
Speaker 1: on issues in data science and responsible A I. We
Speaker 1: are very excited to have her on the podcast to
Speaker 1: share her expertise on Intel's ethics in their A I
Speaker 1: development
Speaker 1: Ria. Welcome to the show. Thank you,
Speaker 2: Graham. It's awesome to be here.
Speaker 1: I've had a look at your bio and would like
Speaker 1: to know how did you come about to join the
Speaker 1: Intel family?
Speaker 2: Sure, I joined Intel in 2018 when I was 14
Speaker 2: years old as an intern. I had, yes, I had
Speaker 2: an amazing mentor who went through all of the legal
Speaker 2: pages and the review needed to get me to that position.
Speaker 2: So initially, I interviewed with three teams on three different
Speaker 2: areas in the A I space. One of them was
Speaker 2: around A I and healthcare, very theoretic
Speaker 2: and mathematical implications and path finding the other two were
Speaker 2: on software development and profiling. And the next was on
Speaker 2: deep learning optimization specifically. So I did have the opportunity
Speaker 2: to pick the one on optimization for deep learning for hardware.
Speaker 2: And that is how I started off my journey at
Speaker 2: Intel and got introduced to it. The interplay between hardware
Speaker 2: and software is something that always drew my attention. So
Speaker 2: when I was able to work on that as part
Speaker 2: of my first role as an intern, I was really excited.
Speaker 1: OK, great. So now uh I understand that you're a
Speaker 1: software A I architect.
Speaker 1: Can you just give an overview of what that entails
Speaker 2: as a software architect? Today? I have a couple of
Speaker 2: roles and responsibilities corresponding to the latest and greatest, which
Speaker 2: is very exciting to me in my day to day.
Speaker 2: The first is generative A I. So looking at and
Speaker 2: taking into account the different software optimizations that we're planning
Speaker 2: for generative A I, how the workloads are shaping changes
Speaker 2: in the algorithms over time as well as also the
Speaker 2: associated mechanisms that we see that are
Speaker 2: in touch with them as an evangelist. I also get
Speaker 2: to work on top of my software architect role as
Speaker 2: a marketer and an advocate for these technologies. So creating
Speaker 2: very short demos and tutorials for users to quickly grasp
Speaker 2: what exactly is going on with this model. How can
Speaker 2: I use it in my day to day? How can
Speaker 2: I put it to my use case. So a lot
Speaker 2: of the focus today for me is on gender to
Speaker 2: A I, I also look into ethical and explainable A
Speaker 2: I tools and technologies as part of my path finding.
Speaker 1: Yeah, I've been using generative A I apps to do research,
Speaker 1: creating podcast, artwork and experimented with creating music. So this
Speaker 1: leads me into asking you what's your definition of artificial intelligence?
Speaker 1: And maybe some examples of where we're seeing it as
Speaker 1: a central topic in the tech world.
Speaker 2: The way that I like to define it is something
Speaker 2: I copied over actually from our recent regulations on A
Speaker 2: I around how A I models are agents or systems
Speaker 2: that are capable of consuming and producing data in an
Speaker 2: environment and also taking actions that can in turn influence
Speaker 2: our decisions. There's a lot of use cases for them everywhere, healthcare, retail,
Speaker 2: et cetera.
Speaker 1: Yeah. When I talked with uh people even in the
Speaker 1: tech world, there's a lot of confusion around OK, you've
Speaker 1: got algorithms, you've got A I, you've got machine learning
Speaker 1: perhaps if you could start with maybe some of the
Speaker 1: difference between algorithms versus say A I. What do you
Speaker 1: see as the difference between the two
Speaker 2: typical algorithms I'd say are based off of certain schemes
Speaker 2: that we're already aware of with machine learning. You have
Speaker 2: these new paradigms that are coming in and completely spinning
Speaker 2: the narrative, things like continual learning, very large models, different
Speaker 2: types of state machines altogether, depending on the application you
Speaker 2: integrate it into. So I would say there are some
Speaker 2: fundamental differences that are coming in between algorithms and machine
Speaker 2: learning models on that front when it comes to use
Speaker 2: cases application and of course implementation as well.
Speaker 1: And where I see the power is sort of combining
Speaker 1: the traditional sort of if then else algorithms uh with
Speaker 1: A I. And I'm just wondering if you've seen any
Speaker 1: sort of practical applications merging of all these techniques.
Speaker 2: Yes. And I'm very interested in composite A I. It's
Speaker 2: something that I'm getting to work on a lot more
Speaker 2: in my day to day. And something that we're actually
Speaker 2: doing a demo for at intel innovation where we are
Speaker 2: chaining multiple large language models together. The way I see
Speaker 2: composite A I is being able to tie together multiple
Speaker 2: models as part of a
Speaker 2: interface or an application with chaining models. I see it
Speaker 2: as a subset of composite A I where you have
Speaker 2: models that are linked to each other and have dependencies
Speaker 2: on their inputs and outputs. It can be sometimes a
Speaker 2: nightmare to get the dependencies altogether because you have cascading
Speaker 2: models one after the other dependent on each is output,
Speaker 2: but it is possible and it does give you a
Speaker 2: lot of applications and opens up the possibilities where you
Speaker 2: can get to a very nice user interface that users
Speaker 2: can interact with. Developers can build upon businesses and other
Speaker 2: communities can just leverage and adopt that is giving you
Speaker 2: a lot of capabilities at once with ease of deployment.
Speaker 1: Oh,
Speaker 1: that's good. Now, turning to the ethics side of it,
Speaker 1: which you've done quite a lot of thinking and work
Speaker 1: in how would you define ethics in A I
Speaker 2: with ethical A I, the definition that I like to
Speaker 2: adopt is socio technical development of A I systems and
Speaker 2: that involves societal and technical aspects, but really focusing on
Speaker 2: the implications and the intentions with these
Speaker 1: algorithms. In terms of when you're talking with your peers
Speaker 1: and colleagues, it has been a lot of discussion and
Speaker 1: talk about trying to have a uniform ethical framework that
Speaker 1: at least gives a common language into, you know, when
Speaker 1: you're discussing these sorts of things related to ethics in
Speaker 1: A I,
Speaker 2: there are common frameworks that are in place. Most of
Speaker 2: them are centered around implications and intention and how we
Speaker 2: structure that around certain technologies. Right now. It's very popular
Speaker 2: for applications, generative A I where we see these frameworks
Speaker 2: being put into place around, let's look at the inputs,
Speaker 2: the outputs and then the overall modeler framework. And this
Speaker 2: may
Speaker 2: simplistic, but it really is boiled down to these very
Speaker 2: simple elements. Similarly for other A I domains that are
Speaker 2: outside of generative A I like object detection, it's very
Speaker 2: much focused on what is the particular use case? For example,
Speaker 2: is it something that is of high risk like health
Speaker 2: care applications or surveillance or is it something that's a
Speaker 2: bit lower risk like content creation
Speaker 2: and then seeing how exactly our user experience and our
Speaker 2: development of those models is echoing ethical A I principles.
Speaker 2: So I would say like to summarize, there are different
Speaker 2: frameworks and summaries that we apply. But of course, the
Speaker 2: templates need to be flexible when we're talking about ethical
Speaker 2: A I for these new A I models.
Speaker 1: how do you go about ensuring that your staff and
Speaker 1: your engineers and your product managers
Speaker 1: actually embed that ethical framework into its A I development.
Speaker 2: Sure, it's such a challenging problem even to describe as
Speaker 2: well as you're mentioning it, you know, there's so many
Speaker 2: different things that you can actively do, right? Like as
Speaker 2: you mentioned, policies, assessments, et cetera. So at Intel, we
Speaker 2: take a multiple approaches towards it. The one thing that
Speaker 2: we very heavily emphasize on is internal governance. And Laman Nachman,
Speaker 2: who's my mentor and also leading the responsibly
Speaker 2: efforts at Intel very neatly and concisely describes them as
Speaker 2: guardrails that we have internally in place. And these are
Speaker 2: really guidelines that are designed to help our developers, engineers, managers,
Speaker 2: and our communities and marketers, et cetera. Understand the implications
Speaker 2: again of what exactly are we producing in terms of
Speaker 2: the content? What are some technical solutions that we can
Speaker 2: instill mid pipeline or early on before starting the effort
Speaker 2: when we're getting started with A I development efforts
Speaker 2: and I would say that that's the core process that
Speaker 2: we focus on. We're also very heavily invested in technological development,
Speaker 2: whether that's through the deep fake detection work that L
Speaker 2: Deir and team are taking on um explainable A I tools,
Speaker 2: et cetera. So really trying to approach this from a
Speaker 2: governance perspective internally, from a tooling perspective, what we can
Speaker 2: provide to the developer community and our customers and to
Speaker 2: partners and from a third perspective, regulations, how do we
Speaker 2: influence the industry at large and help contribute to discussions
Speaker 1: that's really good. And
Speaker 1: you mentioned the work of Lama Nachman and we're actually
Speaker 1: going to be talking with her in an upcoming episode
Speaker 1: this season. So I'm looking forward to asking her about
Speaker 1: this as well. But I think you've said the key phrase,
Speaker 1: deep fake, so I might switch the to that side
Speaker 1: of things. So in terms of the society and, and
Speaker 1: culture in general, um there's some people that are hesitant
Speaker 1: about A I particularly around A I limiting jobs, you've
Speaker 1: got deep fakes. I've actually created a clone of my voice.
Speaker 1: What do you try and do to reassure people who
Speaker 1: have hesitations?
Speaker 2: I'm definitely not,
Speaker 2: I would say not directly enthusiastic about technologies that are
Speaker 2: allowing for passing off as another person for copying and pasting.
Speaker 2: Essentially in certain cases, we see the development of those
Speaker 2: technologies for a certain use case and then it does
Speaker 2: start to stray away from that into some of these
Speaker 2: newer kind of applications that are scary as you shared.
Speaker 2: So when it comes to reassuring individuals, my family, my
Speaker 2: community as well and the industry at large, I think
Speaker 2: that it's definitely a problem to see in a straightforward way.
Speaker 2: Honestly, without the hype surrounding it, there is a levity
Speaker 2: associated with the disadvantages of the technology that we do
Speaker 2: need to consider. We also do see the benefits of
Speaker 2: them for different things, whether that's improving your ease of
Speaker 2: using it, just being able to communicate with others. From
Speaker 2: my perspective, what I try to do in my space
Speaker 2: is to look at an honest assessment of the technology,
Speaker 2: which is very common in the ethical A I domain.
Speaker 2: And to see what exactly is it really contributing to
Speaker 2: the problem statement and if it isn't contributing to it,
Speaker 2: then do we need it?
Speaker 1: And in terms of intel's I guess method or communication
Speaker 1: with the society and people at large, are they working
Speaker 1: on things to help people?
Speaker 1: I feel a little bit more comfortable about this new
Speaker 1: world we're moving into.
Speaker 2: Yes. And we, we tackle it from a couple of
Speaker 2: different fronts. We've got some amazing teams working on different
Speaker 2: parts of the puzzle. One of them is democratization where
Speaker 2: one of the challenging things about A I from an
Speaker 2: ethical A I perspective, but also in general,
Speaker 2: from a development perspective is being able to give communities
Speaker 2: access to the technology so that they can test it
Speaker 2: and validate it. I've been speaking about ethical A I
Speaker 2: for about two years now or so. Last year, we
Speaker 2: really didn't have the same amount of tools and techniques
Speaker 2: that we have this year and also the popularity of
Speaker 2: testing and validating A I systems, right?
Speaker 2: We always understand and I think many companies and organizations
Speaker 2: understand it's not a one size fits all solution for
Speaker 2: ethical A I. Um you know, many companies and organizations
Speaker 2: are trying to do their best. So I would say
Speaker 2: that again that, that push back that community that we're
Speaker 2: trying to create around ethical A I is critical for
Speaker 2: us going forward to be able to better build solutions.
Speaker 1: Has there been any case studies within intel that you
Speaker 1: could share that maybe there was a real challenging ethical
Speaker 1: conundrum
Speaker 1: uh for producing A I software and you know, how,
Speaker 1: how was it resolved? How did you work
Speaker 1: through it?
Speaker 2: Generative A I is definitely a very big one. So
Speaker 2: we're always actively cautious about the types of implications of
Speaker 2: our technology, whether or not we can incorporate disclaimers or
Speaker 2: clarify on the intent of it as well. And um Graham,
Speaker 2: one of my favorite parts
Speaker 2: of ethical A I from a technical perspective in terms
Speaker 2: of solutions is something called model cards. Model cards, clarify
Speaker 2: a very simple theme around ethical A I which is,
Speaker 2: you know, figure out what exactly is the intention the
Speaker 2: core assumptions and the development that went behind the model
Speaker 2: and what you're going to use it for as part
Speaker 2: of deployment.
Speaker 2: And I think that for me personally, I see that
Speaker 2: that theme is conveyed as part of our efforts in
Speaker 2: generative A I, there's a lot of challenging things out
Speaker 2: there when it comes to image generation, copyright, et cetera
Speaker 2: or even, you know, object detection related technologies for retail.
Speaker 2: If you have solutions like intelligent cue management or automated
Speaker 2: self checkout, it makes sense. But you know, how do
Speaker 2: we keep it from proliferating otherwise?
Speaker 1: And what sort of work is going on with inclusive
Speaker 1: A
Speaker 2: I diversity of state
Speaker 2: is critical for the A I models that we're building today,
Speaker 2: whether that's detection of skin agnostic of skin tone or
Speaker 2: being able to adapt to different folks with different accents.
Speaker 2: So at intel and again, across the industry, I think
Speaker 2: a lot of the efforts are really about making sure
Speaker 2: we have the right people on board, the right experts
Speaker 2: with different backgrounds, we're able to contribute to the technologies.
Speaker 1: One
Speaker 1: thing when I started looking into machine learning very quickly,
Speaker 1: I got a sense of,
Speaker 1: you know, being a traditional engineer, you kind of go OK,
Speaker 1: input output and you kind of know what's in the
Speaker 1: in the black box to transform it. When I started
Speaker 1: working with A I and some machine learning code, I
Speaker 1: couldn't get a sense of that 1 to 1 kind
Speaker 1: of mapping of what the output is to input and
Speaker 1: that comes to the to transparency and uh explainability of
Speaker 1: A I algorithms.
Speaker 1: What are you seeing and also what is intel seeing
Speaker 1: around trying to make that understandable to the end users.
Speaker 2: It's a really interesting question because explainability is one of
Speaker 2: the first topics that we think about when we think
Speaker 2: about responsibly. I and I agree the black box metaphor
Speaker 2: has been used so many times um because it's true.
Speaker 2: But the key idea is about demystifying what exactly is
Speaker 2: going on within the model. Whether that is the internal representation, again,
Speaker 2: the data that it's pulling from how the data is
Speaker 2: being leveraged feature and
Speaker 2: importance, et cetera. There's also an added consideration to explain
Speaker 2: ability around surfacing that to an end user. For them
Speaker 2: to understand why the model made a decision I would
Speaker 2: say with Intel, we're approaching it in a couple of
Speaker 2: different ways. And I'm just, I'm very excited to see
Speaker 2: how can different experts approach our problems. We have a
Speaker 2: dedicated suite of technologies for explainability. I led a team
Speaker 2: that was developing one of these for Intel Cno where again,
Speaker 2: you're getting that
Speaker 2: internal representation analysis, Saliency maps and other technologies for explainability.
Speaker 2: We also incorporate transparency and explainability into our algorithm. So
Speaker 2: whether that's being able to visualize what's going on again,
Speaker 2: saliency maps or you know, really good user experience user
Speaker 2: interface to figure out why am I being surfaced this
Speaker 2: particular prediction or decision from a model? I'd say that's
Speaker 2: a couple of the ways that we are integrating and
Speaker 2: thinking about explainability at Intel.
Speaker 1: You're listening to technically speaking an Intel podcast. We'll be
Speaker 1: right back.
Speaker 1: Welcome back to technically speaking an Intel podcast.
Speaker 1: One of the obviously the big things is around the
Speaker 1: privacy and security of data. Perhaps you could outline some
Speaker 1: of the new techniques and new initiatives out in the
Speaker 1: industry to try and use the power of A I
Speaker 1: but still protect companies, information and and
Speaker 1: data.
Speaker 2: I would say there's mechanisms like differential privacy and many others,
Speaker 2: homomorphic encryption. These were incredibly popular two years ago, you
Speaker 2: kind of don't hear them a lot now. So again,
Speaker 2: the hype is it it depends on the technology of
Speaker 2: the day.
Speaker 2: But yes, localization is a key thing. It's actually something
Speaker 2: I have the opportunity to look at now as part
Speaker 2: of my role around hybrid A I edge versus cloud
Speaker 2: edge and cloud. So there's a number of different parameters
Speaker 2: and assumptions that we can start to make at the
Speaker 2: edge around localization privacy of data, not necessarily having
Speaker 2: to communicate it back to the cloud that are changing
Speaker 2: the way that we think about data privacy and security
Speaker 2: for A I models Federated learning is another paradigm like this.
Speaker 2: So to put it shortly, I'd say there are mechanisms
Speaker 2: that are coming up in place, but there is still
Speaker 2: more needed emphasis on security and privacy, more development for technologies,
Speaker 2: et cetera.
Speaker 1: OK. So just to extend that just a little bit more.
Speaker 1: So say if you're meeting with an executive saying, I've
Speaker 1: been hearing all about large language models and I was
Speaker 1: talking to my colleague uh in another company and they're
Speaker 1: starting to use chatbots with within their organization and using
Speaker 1: the power of that is that related to large language models,
Speaker 1: but fine tuning it to their own corporate data in
Speaker 1: their own
Speaker 1: servers. If you like I sort of on the right track.
Speaker 2: Yes, that is a perfect use case. And thank you
Speaker 2: for bringing that up, you know, centralization of data on
Speaker 2: your server. There's also red teaming um gram that's worth
Speaker 2: mentioning where you're testing your model or your system thoroughly
Speaker 2: with the generative A I space. There's come to life,
Speaker 2: a lot of different types of red teaming approaches including
Speaker 2: prompt injection and many others, which is really a
Speaker 2: being able to test and mock the kinds of inputs
Speaker 2: that adversaries would provide to your model and figure out
Speaker 2: how the model is going to behave. What are its
Speaker 2: strengths and weaknesses, et cetera. Of course, the compute needed
Speaker 2: for that is another story. But in addition to that,
Speaker 2: there's also again, the testing and validation approaches. So red
Speaker 2: teaming is really critical to that validating how susceptible your
Speaker 2: model is to potential attacks, whether it's biased, et cetera.
Speaker 2: So lots of, lots of cool and interesting approaches coming up.
Speaker 2: Exactly as you noted, that's a key example.
Speaker 1: So going back on the ethics side of things, what
Speaker 1: are some of the arguments for a corporation, an organization
Speaker 1: to have a clear set of code of ethics and
Speaker 1: is intel helping companies establish those sorts of guidelines and frameworks.
Speaker 2: There is a number of different best practices that organizations
Speaker 2: can incorporate today for responsible A I. One of them
Speaker 2: is the internal governance assessments that we talked about, which
Speaker 2: is a step by step process to checking where A
Speaker 2: I is used in your organization. How is it being
Speaker 2: shipped outside? What's your go to market strategy? What's your
Speaker 2: change management strategy, etcetera.
Speaker 2: So in terms of Intel's contributions, we're very excited and
Speaker 2: passionate about communication with customers and partners and communities in
Speaker 2: general around. What exactly can we do to help with
Speaker 2: the ethical A I development that can include, you know,
Speaker 2: potential compute platforms that help with running this type of solutions, preprocessing,
Speaker 2: post processing. What exactly
Speaker 2: you need towards that? Or if we have developers working
Speaker 2: with Intel Open Veno and I work in the Open
Speaker 2: Vino team right now. We want to know what makes
Speaker 2: it easier for developers to be able to run these
Speaker 2: models and deploy them their feedback in terms of, you know, hey,
Speaker 2: you know, is this challenging to use? I don't know
Speaker 2: how this is working. Um something that I do as
Speaker 2: part of my evangelism team is again, helping contribute to that.
Speaker 2: So I would
Speaker 2: say that as part of the practices, there's a number
Speaker 2: of different things that we do today with solutions with guardrails,
Speaker 2: with assessments. And at Intel, we're trying to help with
Speaker 2: the communication, the establishment of these elements as well as
Speaker 2: the technical solutions and how we can help build foundations
Speaker 2: that our partners, customers, the community and industry can take
Speaker 2: from there.
Speaker 1: You mentioned that you're part of the Intel Open Vino group.
Speaker 1: Perhaps you could spend a bit of time just explaining
Speaker 1: what that group does and what your role in. It is.
Speaker 2: Sure. The Intel Open Veno group is a team dedicated
Speaker 2: to helping provide capabilities and developing our Open Veno toolkit.
Speaker 2: The toolkit is centered around computer vision related applications and
Speaker 2: it's recently expanded over five years to generative A I.
Speaker 2: And it is really centered around taking models in many
Speaker 2: different frameworks like Pytorch, tensorflow, caras, et cetera and converting
Speaker 2: and optimizing them to an intermediate representation format that you
Speaker 2: can deploy on different hardware, including Intel CP US, GP
Speaker 2: US and other types of hardware.
Speaker 1: And have you seen any I guess impact on, on
Speaker 1: innovation to, to put it bluntly does having a code
Speaker 1: of ethics, put a brake on innovation and
Speaker 1: for individual engineers, does it leave them feeling? Oh, maybe
Speaker 1: I shouldn't try these things. Is it a hindrance?
Speaker 2: The big question I've encountered this question before but my,
Speaker 2: my answer to it is no, it is not. Because
Speaker 2: um what again, my personal opinion and what I've also
Speaker 2: seen at Intel and through my colleagues, mentors and industry
Speaker 2: academia and other circles
Speaker 2: at the core of innovation is certain themes like improving
Speaker 2: quality of life, et cetera. And as a part of
Speaker 2: that human rights responsible A I adoption of technologies and
Speaker 2: understanding why you're using technologies with awareness, those are all
Speaker 2: key attributes. So I would say if we're able to
Speaker 2: design the process in a way that's efficient, that is
Speaker 2: incorporating the minimum
Speaker 2: requirements and has the flexibility to grow with the technology,
Speaker 2: then we're doing it right? And it is not a hindrance.
Speaker 2: Time to go to market is a key item. However
Speaker 2: responsible A I process is while they may take time,
Speaker 2: they don't necessarily have to hinder that goal if they're
Speaker 2: streamlined and done efficiently. The onus is on all of
Speaker 2: us to be able to contribute to that kind of
Speaker 2: strategy or development of that strategy.
Speaker 1: And in terms of the
Speaker 1: A I evolving over the next five years, you know,
Speaker 1: where do you see it going?
Speaker 2: Human centered A I, that is my personal opinion on it.
Speaker 2: I've done a lot of research on it. I also
Speaker 2: had the opportunity to author publication on it. Technology that's
Speaker 2: centered around the human experience that is contributing to the
Speaker 2: way that we think that we act and that we
Speaker 2: interact with others
Speaker 2: I would say is the key thing. And for me,
Speaker 2: that's the most exciting applications, whether that's smart care robots
Speaker 2: for the elderly, using generative A I for health care applications,
Speaker 2: identifying new protein folding related techniques or something similar. But
Speaker 2: centered around the human experience, I would say. So Human
Speaker 2: Centered A I is a good theme for that overarching journey.
Speaker 1: Yeah, the Human Centered A I is a very interesting concept.
Speaker 1: And have you seen any examples, either in the start
Speaker 1: up community or within Intel or in the industry where
Speaker 1: you've given some examples? But is any that are actually
Speaker 1: like kind of in production today?
Speaker 2: So we have some accessibility research that we've done with Intel.
Speaker 2: You know, Laman Kachin also leads the human computer interaction
Speaker 2: lab and we see a lot of I see a
Speaker 2: lot of great research coming out of that around accessibility,
Speaker 2: hearing related initiatives, et cetera. I would say that they're
Speaker 2: in the process of being researched right now to my
Speaker 2: knowledge across the industry of technologies that we can actively
Speaker 2: put in place. But there are blueprints in place for
Speaker 2: Human Centered A I
Speaker 2: technologies. So it will be exciting to see how they evolve,
Speaker 2: how you know, we take into consideration newer models like
Speaker 2: generative A I that again, popularity just kind of popped up,
Speaker 2: but they've been around for a while. So we need
Speaker 2: to see how the technology adapts, but I think it
Speaker 2: will stay true. To like the test of time in
Speaker 2: five years time and then we will be able to
Speaker 2: see and interact with A I applications that are centered
Speaker 2: around our experiences around nature, et cetera.
Speaker 1: How do you differentiate the two between
Speaker 1: the ethical A I and responsible A I? Um because
Speaker 1: in my mind, it's kind of a little bit in
Speaker 1: a little bit jumbled.
Speaker 2: Sure, I use the term actually in overlap, uh just
Speaker 2: my personal bias towards you. But I, I have seen
Speaker 2: that there are differences, there's been multiple efforts to establish
Speaker 2: a nomenclature in the ethical A I domain. So responsible
Speaker 2: A I is seen more as the internal governance, the
Speaker 2: processes and practices that we put towards A I. Whereas
Speaker 2: ethically I is seen as really maybe kind of a
Speaker 2: combination of the societal and technical aspects as I shared earlier.
Speaker 2: So responsibly I in a sense is the accountability and
Speaker 2: responsibility part of it.
Speaker 1: Uh I talked earlier about the future of A I.
Speaker 1: How is intel gonna be part of that wave in
Speaker 1: terms of its programs and solutions for customers
Speaker 2: A I is a key inflection point for us. We're
Speaker 2: excited to ride the new wave, collaborate with our again, partners,
Speaker 2: customers communities and um see what we can do next.
Speaker 2: What's the next great big thing?
Speaker 2: Uh Generative A I is definitely a key focus for us.
Speaker 2: It's what our customers want, it's what developers want and
Speaker 2: it's what users want as well for their content creation
Speaker 2: and many, many other needs. So we're very focused on that.
Speaker 2: We're also incredibly focused on the compute. I see a
Speaker 2: lot of and get to work with a lot of
Speaker 2: wonderful engineers that are very passionate about solving these problems
Speaker 2: at hand. Specifically these um because there's, you know, so
Speaker 2: much that you can do a lot of problems in
Speaker 2: the LLM and generative A I
Speaker 2: based around, you know, large models, large footprint, changing outputs,
Speaker 2: not a lot of predictability, challenging to benchmark, etcetera. So
Speaker 2: I think that Intel is working on and actively positioned
Speaker 2: to help our customers. Developers provide these types of optimizations,
Speaker 2: the right kind of compute et cetera for, for the
Speaker 2: new wave of A I but outside of generative A
Speaker 2: I also, there's a lot of other A I applications
Speaker 2: that we're aware of human centered A I, et cetera
Speaker 2: that we are also actively working on. So we're ready.
Speaker 1: Oh, that's, that's good to hear. I've definitely learnt quite
Speaker 1: a lot. So thank you very much for your time.
Speaker 2: Thank you, Graham. Appreciate it.
Speaker 1: I would like to thank my guest Ria Chu for
Speaker 1: joining me today on this special episode of technically speaking,
Speaker 1: an Intel podcast
Speaker 1: ethics and artificial intelligence are so important right now. And
Speaker 1: what I've learnt from today's discussion with R A, having
Speaker 1: a code of ethics can be an important standard, especially
Speaker 1: when it comes to deep fakes companies in the media
Speaker 1: industry should have a rule about never Impersonating someone without
Speaker 1: their knowledge. In my experience, I've been able to clone
Speaker 1: my own voice within a day and it's a pretty
Speaker 1: good quality
Speaker 1: for me as an engineer and a technologist. I think
Speaker 1: that's really interesting. However, it does throw up a lot
Speaker 1: of questions around ethics and whether we should do these things.
Speaker 1: The other thing Ria touched on is human centered A
Speaker 1: I And that's really interesting from my perspective, I think
Speaker 1: technology has moved towards trying to be human centered. And
Speaker 1: it's good to see that A I wave that is
Speaker 1: coming is still trying to keep humans as the center
Speaker 1: of any product and technology design.
Speaker 1: And talking with Rea really did hit home to me
Speaker 1: that it is artificial intelligence, but I am looking at
Speaker 1: the way that it can actually augment us. I think
Speaker 1: that it will augment our jobs. I don't think on
Speaker 1: balance that it will take away jobs. You only have
Speaker 1: to look back in history from the printing press to
Speaker 1: the loom. The A I wave that we're going through
Speaker 1: now is just another evolution of us as a species.
Speaker 1: And I love discussion around the ethics and the philosophy
Speaker 1: of A I,
Speaker 1: I hope it will continue.
Speaker 1: And that's all for our first episode. Thanks so much
Speaker 1: for joining me today. Please join us on Tuesday October
Speaker 1: 17th for the next episode where we speak with experts
Speaker 1: on the way A I is innovating agribusiness solutions. You
Speaker 1: can follow me on linkedin and Twitter or X with
Speaker 1: the handle at Graham Class or check the show notes
Speaker 1: page for links. This has been technically speaking, an Intel podcast,
Speaker 1: technically speaking was produced by Ruby Studios from iheartradio in
Speaker 1: partnership with Intel and hosted by me Graham Class.
Speaker 1: Our executive producer is Molly. So our EP of post
Speaker 1: production is James Foster and our supervising producer is Nikia Swinton.
Speaker 1: This episode was edited by Ciara Spring and written and
Speaker 1: produced by Tyree Rush.