Speaker 1: Welcome to the deep dive. Today, we're really getting into
Speaker 1: the weeds of artificial intelligence.
Speaker 2: Specifically for dot net developers.
Speaker 1: Right exactly, we're looking at how you can actually use
Speaker 1: things like speech, language and search.
Speaker 2: All powered by Microsoft's cognitive services based on the source
Speaker 2: material we've got.
Speaker 1: Yeah, so think of this as well, moving past the buzzwords.
Speaker 2: Right from the big ideas down to the practical tools
Speaker 2: you can use to build smarter apps.
Speaker 1: Our goal here is, you know, to cut through the noise,
Speaker 1: pull out the really key concepts, get.
Speaker 2: Those aha moments, maybe uncover some surprising bits.
Speaker 1: And do it without you needing to like write a
Speaker 1: single line of code. Right now, it's.
Speaker 2: About understanding the essentials, the foundation, the services, so you
Speaker 2: know what's possible.
Speaker 1: Okay, so let's lay that foundation AI. It's everywhere often
Speaker 1: sounds like science fiction. What's the real deal.
Speaker 2: Well, at its core, it's about building systems that do
Speaker 2: things needing human.
Speaker 1: Like intelligence, but maybe not sentient robots just yet.
Speaker 2: Hah No, it's much more grounded. Think specific tasks, specific
Speaker 2: capabilities that are available now.
Speaker 1: And getting here wasn't exactly a smooth ride, was it.
Speaker 1: There were these AI winters.
Speaker 2: That's right, two main periods, sort of mid seventies to
Speaker 2: early eighties and then again late eighties to early nineties.
Speaker 2: What happened there, Basically, the hype got way ahead of
Speaker 2: the actual technology. Promises were made that just couldn't be
Speaker 2: delivered with the computing power.
Speaker 1: Back then, so uh, funding dried up, progress stalled exactly.
Speaker 2: People got disillusioned. But then things started picking up again.
Speaker 1: Why what changed?
Speaker 2: Computers got faster, cheaper, you know Moore's law and action.
Speaker 2: Suddenly some older ideas became.
Speaker 1: Feasible, and we started seeing these specialized systems achieving big things.
Speaker 2: Yes, like IBM's Deep Blue beating Gary kaspar Off at
Speaker 2: chess in ninety seven.
Speaker 1: That was huge, a machine beating the world champ in
Speaker 1: such a complex game.
Speaker 2: It showed that focused DAI could really excel. But the
Speaker 2: current boom that really kicked off more recently.
Speaker 1: Driven by the tech giants and testing heavily.
Speaker 2: Absolutely, and then you had moments like IBM Watson winning
Speaker 2: Jeopardy Yeah in twenty eleven.
Speaker 1: Right, that really showcased natural language processing in a big way.
Speaker 2: It did, and after that companies really started productizing these
Speaker 2: AI capabilities, making them available as services.
Speaker 1: Which brings us to today, where AI feels like it's
Speaker 1: baked into so much tech.
Speaker 2: Your phone, websites, even video game characters reacting to what
Speaker 2: you do.
Speaker 1: So it went from big dreams, some setbacks, and now
Speaker 1: it's practical, usable tech.
Speaker 2: Pretty much, and that practicality is changing how we even
Speaker 2: interact with computers.
Speaker 1: Let's talk about user interfaces. We started way back with
Speaker 1: the command line, the CLI.
Speaker 2: Powerful, yeah if you knew the commands, but super intimidating
Speaker 2: for beginners.
Speaker 1: Like learning a secret code kind of yeah.
Speaker 2: Then came the GUI, the graphical user interface.
Speaker 1: Total game changer, Windows icons, the mouse.
Speaker 2: Building on work from places like Xerox PRC, then popularized
Speaker 2: by Apple and Microsoft. It made computing accessible.
Speaker 1: You could see what you were doing exactly.
Speaker 2: But now there's another shift happening towards conversation.
Speaker 1: The conversational user interface or see y right.
Speaker 2: The idea is you just talk or type to the system,
Speaker 2: like messaging a friend. No clicking through menus.
Speaker 1: So ordering pizzas just typing get me a large pepperoni.
Speaker 2: That's the goal, simple natural interaction. Messaging apps have made
Speaker 2: us really comfortable.
Speaker 1: With this, Okay, but that sounds like it needs some
Speaker 1: serious smarts behind it.
Speaker 2: It absolutely does. This is where AI is crucial, specifically
Speaker 2: Natural language understanding NLU.
Speaker 1: Because the system has to figure out what you actually mean,
Speaker 1: not just what you typed.
Speaker 2: Precisely, it needs NLU in the back end to interpret
Speaker 2: that conversational input.
Speaker 1: So what's the weather and forecast for today mean the
Speaker 1: same thing to the system.
Speaker 2: A good NLU system should understand that. Yes, it identifies
Speaker 2: the user's goal, the intent.
Speaker 1: It pulls out the important bits of information.
Speaker 2: Those are the entities. So weather in London tomorrow. The
Speaker 2: intena is get weather entities are London and tomorrow.
Speaker 1: Seems intuitive for us, but you mentioned CUIs aren't perfect.
Speaker 2: There are challenges, Oh definitely. They struggle with really complex,
Speaker 2: nuanced conversations, and there are risks.
Speaker 1: Remember Tay, Microsoft's Twitter bot. Oh yeah, that one sideways fast.
Speaker 2: It learned from interactions, but unfortunately it learned toxic stuff
Speaker 2: very quickly and started spewing offensive tweets.
Speaker 1: Had to be shut down almost immediately.
Speaker 2: A really stark reminder that AI learning from the real
Speaker 2: world needs careful controls. You can't just let it loose
Speaker 2: without safeguards.
Speaker 1: So maybe it mixes better for now combining CUI and GUI.
Speaker 2: Yeah, the source suggests a hybrid approach often makes sense.
Speaker 2: Use conversation for simple things, stick to graphical interfaces for
Speaker 2: more complex tasks.
Speaker 1: Okay, let's dig into that NLU piece more. It's fundamental.
Speaker 1: Why is it considered an AIHRD problem.
Speaker 2: Because human language is just incredibly complex and subtle. Getting
Speaker 2: a machine to grasp it properly isn't just one algorithm.
Speaker 1: It's like computer vision or machine translation requires lots of
Speaker 1: different techniques working together.
Speaker 2: Exactly, there are multiple layers of difficulty like way, Well, first,
Speaker 2: there's syntax, the grammar, the structure of sentences. Machines need
Speaker 2: to parse that correctly.
Speaker 1: Okay, sentence rules makes sense.
Speaker 2: Then semantics. That's the meaning of words and sentences, synonyms,
Speaker 2: words with multiple meanings.
Speaker 1: I like apples versus I'm fond of apples, same meaning,
Speaker 1: different words.
Speaker 2: Right, The machine needs to get that underlying concept.
Speaker 1: Any sounds tricky, what's.
Speaker 2: Next, pragmatics. This is maybe the toughest bit. It's understanding
Speaker 2: the implied meaning the context that's not being explicitly sex Exactly,
Speaker 2: if I say wow, it's hot in here, I might
Speaker 2: actually mean can you open a window? The machine needs
Speaker 2: situational awareness.
Speaker 1: Which computers usually lack. They don't have our common sense
Speaker 1: or world knowledge precisely.
Speaker 2: And then you've got just plain.
Speaker 1: Ambiguity words meaning different things.
Speaker 2: Yeah, like bank riverbank or financial bank, or sentences that
Speaker 2: can be read multiple ways like I saw a man
Speaker 2: on a fill with a telescope.
Speaker 1: The classic who has the telescope? Right?
Speaker 2: And finally just the sheer variation in language spoken versus
Speaker 2: written dialects, slang, typos. It's messy, very messy for a
Speaker 2: machine to handle consistently.
Speaker 1: So syntax, semantics, pragmatics, ambiguity variation quite a challenge. Were
Speaker 1: their early attempts to crack this.
Speaker 2: Yeah, some famous ones Eliza back in the sixties mimicked
Speaker 2: a therapist using pattern matching. It seems smart, but didn't really.
Speaker 1: Understand, more like clever tricks.
Speaker 2: Kind of a bigger step was SHRDLU around nineteen seventy.
Speaker 1: SHRDLU what did it do?
Speaker 2: It operated in a tiny virtual world of blocks. You
Speaker 2: could tell it pick up the blue pyramid or ask
Speaker 2: questions about the blocks, and understood within that very limited world. Yes,
Speaker 2: remarkably well. It showed that NLU was possible if you
Speaker 2: constrain the domain significantly.
Speaker 1: Okay, so NLU is hard but vital. How do developers
Speaker 1: like our listeners actually use it today without building it
Speaker 1: all themselves.
Speaker 2: That's where cloud services come in, like Microsoft's LUIS Language
Speaker 2: Understanding Intelligence Service elleweeds.
Speaker 1: So it's like NLU as a service pretty much.
Speaker 2: You don't need to be the deep learning expert. Your
Speaker 2: job is mainly to train it for your specific application.
Speaker 1: How does that training work?
Speaker 2: You feeded example sentences. They're called utterances that your users might.
Speaker 1: Say things like find me a nearby Italian restaurant.
Speaker 2: Exactly, And for each utterance you tell owe with the
Speaker 2: user's goal. The intent is like fine restaurant, and you
Speaker 2: label the key info the.
Speaker 1: Entities, so Italian would be cuisine type, nearby implies location precisely.
Speaker 2: You provide lots of examples, Louis learns from them using
Speaker 2: machine learning algorithms. Then when a new sentence comes in,
Speaker 2: it predicts the intent and extracts the entities.
Speaker 1: What are the main bits you can figure in LA?
Speaker 2: You define your intents the actions users can take, and
Speaker 2: you define your entities the data points you need.
Speaker 1: Are there different types of entities?
Speaker 2: Yes, quite a few, simple entities are ones you define
Speaker 2: like product category. But Louis also has pre built entities
Speaker 2: which are super useful.
Speaker 1: What do they cover?
Speaker 2: Common stuff like dates, times, numbers, locations, email addresses, percentages.
Speaker 2: Saves you a lot of effort. It already knows how
Speaker 2: to recognize next Tuesday at three pm.
Speaker 1: That's handy. What else?
Speaker 2: You can create composite entities to group related entities like
Speaker 2: an order entity containing item and quantity, and hierarchical entities
Speaker 2: for parent child relationships like person name having first name
Speaker 2: and last name help.
Speaker 1: Structure the extracted data. What about phrase lists?
Speaker 2: Think of them as giving ellewe hints. You list words
Speaker 2: or phrases that are strong indicators for certain intents or entities,
Speaker 2: like a list of all your product names or synonyms
Speaker 2: for book a meeting.
Speaker 1: It helps boost the signal for important terms exactly.
Speaker 2: And then there's active learning. This is really important.
Speaker 1: After you launch, what does that do?
Speaker 2: Elliwei identifies utterances it wasn't very sure about. It shows
Speaker 2: them to you. You clarify the correct intents and entities,
Speaker 2: and that feedback helps retrain and improve the model over time.
Speaker 1: So the model gets smarter based on real user interactions.
Speaker 2: Correct It's a continuous improvement cycle and.
Speaker 1: The overall flow for an app Using Alleyway, your.
Speaker 2: App gets the user's text sense, sends it to the
Speaker 2: Louis API. Louis sends back Jason with the predicted intent
Speaker 2: and entities. Your app uses that info to.
Speaker 1: Do the right thing, like calling another API, querying a database, whatever.
Speaker 2: The action is exactly. It integrates nicely with things like
Speaker 2: the Microsoft Bought framework for building chatbots.
Speaker 1: Okay. LAOS handles the core understanding. What other text analysis
Speaker 1: tools are there in cognitive services?
Speaker 2: Several useful ones. There's the Bing's spell check API, just
Speaker 2: basic spell check. It's smarter than that. It's contextual. It
Speaker 2: understands that booking is correct in booking a flight, but
Speaker 2: maybe not somewhere else. It gets proper nouns like Microsoft,
Speaker 2: even if slightly misspelled ah.
Speaker 1: So it considers the surrounding words useful for cleaning up
Speaker 1: U input definitely.
Speaker 2: It even handles some slang and common brand name misspellings.
Speaker 1: What else in the text suite?
Speaker 2: The text Analytics API bundles a few things. Language detection
Speaker 2: figures out what language the text is in, useful for routing,
Speaker 2: support tickets, or filtering content.
Speaker 1: And sentiment analysis that seems really popular.
Speaker 2: It is analyzing if text is positive, negative, or neutral.
Speaker 2: Companies use it constantly for customer reviews, social media monitoring.
Speaker 1: Getting a pulse on customer opinion at scale.
Speaker 2: Right, it usually gives a score like point nine for
Speaker 2: very positive, point one for very negative.
Speaker 1: Does it do summarization too?
Speaker 2: Not exactly summarization, but key phrase extraction pulls out the
Speaker 2: main talking points, the important noun phrases, and topic detection
Speaker 2: can group large amounts of text like reviews, into underlying themes.
Speaker 1: The source also mentioned something called the Web language model
Speaker 1: or web LM.
Speaker 2: Yeah, that's a language model trained on well huge amounts
Speaker 2: of web data from bing. It understands common word sequences
Speaker 2: and probabilities.
Speaker 1: What's that use?
Speaker 2: For things like word breaking, splitting buy tickets now into
Speaker 2: buy tickets now, calculating joint probability? How likely is the
Speaker 2: phrase natural language processing versus say natural language pineapple okay?
Speaker 1: Measuring how natural a phrase sounds.
Speaker 2: And conditional probability predicting the next word given artificial how
Speaker 2: likely is intelligence to follow? This powers things like autocorrect
Speaker 2: and tax suggestions.
Speaker 1: Wow, quite a toolbox for text. Now, what about turning
Speaker 1: speech into text and back.
Speaker 2: That's where the speech APIs come in. Speech to text
Speaker 2: STT converts audio to text text to speech TTS does
Speaker 2: the reverse.
Speaker 1: How does STT work? Generally?
Speaker 2: It analyzes the audio signal, breaks it down into basic
Speaker 2: sound units called phonemes, and uses acoustic and language models
Speaker 2: to figure out the most likely sequence of words. Usually
Speaker 2: gives a confidence score.
Speaker 1: Two and Microsoft's offerings.
Speaker 2: There are standard speech APIs, but the really interesting one
Speaker 2: is the Custom Speech Service criis custom. Howso CRES let's
Speaker 2: use adapt the speech recognition model to your specific scenario.
Speaker 1: What does that mean?
Speaker 2: You can upload your own audio data and accurate transcripts.
Speaker 2: If your app will be used in a noisy factory
Speaker 2: or involves lots of specific jargon or product names, you
Speaker 2: can train a model that's much better at understanding that
Speaker 2: specific audio environment and vocabulary.
Speaker 1: Ah, so you tailor it to overcome background noise or
Speaker 1: specialized language exactly.
Speaker 2: It can make a huge difference in accuracy for specific
Speaker 2: use cases compared to a general purpose model.
Speaker 1: And what about recognizing who is talking?
Speaker 2: That's speaker recognition two main types. Verification confirms if a
Speaker 2: voice matches a known person like voice log in usually
Speaker 2: needs enrollment where the person says specific phrases.
Speaker 1: Okay, one to one matching.
Speaker 2: And identification, which tries to figure out which speaker from
Speaker 2: a pre enrolled group is the one talking. Useful for
Speaker 2: transcription that notes who said.
Speaker 1: What, and for the other way text to speech, making
Speaker 1: the computer talk naturally. Yeah.
Speaker 2: TTS takes text and generates audio. The source mentions ssmls
Speaker 2: each synthesis markup language, what's that for? It lets you
Speaker 2: control how the text is spoken. Things like emphasis, pitch,
Speaker 2: speaking rate, pauses, even pronunciation of specific words helps make
Speaker 2: the synthesized voice sound less.
Speaker 1: Robotic speech text feels like it's gotten way better recently.
Speaker 2: It really has, largely thanks to deep learning bottles, but
Speaker 2: accuracy is still a challenge, especially in noisy places or
Speaker 2: with strong accents. The source notes that even with claims
Speaker 2: of low error rates like Google's four point nine percent,
Speaker 2: that's often in ideal conditions.
Speaker 1: Which is why that custom speech service is valuable for
Speaker 1: bridging the gap in real world scenarios.
Speaker 2: Precisely.
Speaker 1: Okay, shifting focus again, let's talk search and recommendations making
Speaker 1: information findable.
Speaker 2: Right, we have explicit search. You type a query, but
Speaker 2: AI enables implicit search.
Speaker 1: Where the system anticipates what you need.
Speaker 2: Yeah, like Amazon showing customers who bought this also bought
Speaker 2: or related items. It's proactive.
Speaker 1: The source mentioned the three piece of search.
Speaker 2: Right search everywhere, predictive, anticipating needs, proactive, giving answers before
Speaker 2: you ask.
Speaker 1: That's the ideal, and Microsoft has bing APIs for web
Speaker 1: image news search. Let's focus on recommendations though, how do
Speaker 1: those work?
Speaker 2: The main goal is usually to increase sales or engagement
Speaker 2: by suggesting relevant things.
Speaker 1: What kinds are there?
Speaker 2: Frequent bought together FBT is common items often bought in
Speaker 2: the same transaction, like a camera and a memory card
Speaker 2: makes sense. Then item to item, which is a type
Speaker 2: of collaborative filtering. It suggests items based on what other
Speaker 2: similar users liked. People who viewed this also viewed.
Speaker 1: Based on collective behavior.
Speaker 2: And user to item, which is more personalized. It looks
Speaker 2: at your past history, views, purchases to recommend things specifically
Speaker 2: for you.
Speaker 1: How do you build these using the Microsoft service?
Speaker 2: You need data two main types. Catalog data, which is
Speaker 2: info about your items, products, articles, whatever, including features and
Speaker 2: usage data records of user interactions like clicks, purchases, ratings,
Speaker 2: so you feed it.
Speaker 1: Your product list and how people.
Speaker 2: Interact with exactly. Then you train different recommendation models called
Speaker 2: builds on that data. There are specific builds for FBT
Speaker 2: and others like SAR that handle itemed item and user
Speaker 2: to item, and.
Speaker 1: The quality of recommendations depends heavily on that input data.
Speaker 2: Absolutely, good data, good quantity leads to better recommendations.
Speaker 1: The source also mentioned ranking and offline evaluation.
Speaker 2: Ranking is crucial how do you order the results. It's
Speaker 2: often based on relevant scores derived from usage data and
Speaker 2: item features. Offline evaluation lets you test your train models
Speaker 2: on historical data before deploying them to see which build
Speaker 2: performs best.
Speaker 1: Okay, so we've covered a lot of ground from AI
Speaker 1: history to interfaces and LU with LIS text analysis, speech
Speaker 1: tech search recommendations.
Speaker 2: It's quite a journey, but the key takeaway, I think,
Speaker 2: is how these advanced AI capabilities are becoming accessible right.
Speaker 1: Things that needed huge research teams are now available as
Speaker 1: APIs like cognitive services.
Speaker 2: Especially for developers already in the dot net world. It
Speaker 2: lowers the barrier significantly to adding intelligence.
Speaker 1: It makes you really think about where AI is already
Speaker 1: working behind the scenes in.
Speaker 2: Your life or how these tools could reshape industries think
Speaker 2: about customer service, retail, healthcare.
Speaker 1: Definitely yeah, and that brings us to the future. The
Speaker 1: source touches on this idea of AI first organizations.
Speaker 2: Yeah, companies embedding AI into their core strategy, their products,
Speaker 2: how their people work.
Speaker 1: And it addresses the big question about jobs.
Speaker 2: The perspective offered is interesting tasks, not jobs, will be eliminated.
Speaker 2: The focus shifts to how human roles will change and
Speaker 2: work alongside AI.
Speaker 1: Augmented intelligence not just artificial intelligence replacing humans exactly.
Speaker 2: Combining the strengths of both human and machines working together
Speaker 2: can achieve way more than either could alone.
Speaker 1: It's a vision of AI becoming woven into everything, cars, factories, shopping,
Speaker 1: daily life.
Speaker 2: A fundamental transformation.
Speaker 1: So to wrap up, we've seen how AI is evolve,
Speaker 1: how tools like cognitive services make it practical for developers,
Speaker 1: especially with dot net, to build apps that understand language, speech,
Speaker 1: and user needs through search and recommendations.
Speaker 2: It really puts powerful capabilities within reach.
Speaker 1: And thinking about that future, that augmented intelligence idea where
Speaker 1: tasks change and humans partner with AI. Here's a final
Speaker 1: thought for you to consider. If AI is set to
Speaker 1: transform our tasks and merge with our capabilities. What completely
Speaker 1: new roles, new kinds of expertise, or maybe even entirely
Speaker 1: new opportunities might emerge from this human machine partnership in
Speaker 1: the coming years, Things we perhaps can't even quite imagine
Speaker 2: Today, something definitely worth pondering.