AI/ML Fundamentals for Product Managers

Summary

本次演讲由Darris Kumari主讲，讨论了产品经理学习人工智能和机器学习的重要性，以及如何在现有产品和组织中实现AI。演讲涵盖了AI的基本能力、如何作为产品经理理解AI的内部工作原理，并通过实例讲解了监督学习、无监督学习和神经网络在AI领域的作用。强调了AI在提高用户体验、产品差异化和推动收入增长方面的关键作用，同时探讨了实施AI技术的战略方法和潜在风险。

Takeaways

• 🚀 产品管理者应学习AI和ML，以保持竞争力并最大化客户和用户体验。
• 💡 创新的七个关键力量包括流程需求、行业变化、市场变化、新知识、潜在的不一致性和用户期望。
• 🌟 AI和ML可以提供额外的价值，帮助组织获得溢价收入和产品差异化。
• 🛠️ 实施AI技术时，组织可以选择自建或与云供应商合作。
• 🔄 对于AI和ML的采纳，组织结构上可能需要一个集中的平台团队来支持其他产品团队。
• 📈 产品管理者应从用户痛点和旅程出发，思考AI和ML在产品中的应用。
• 🥄 开始AI和ML项目时，建议采用分步方法，包括创意、概念验证和产品化。
• 🔒 在实施AI和ML时，需要考虑法律、数据所有权和用户数据安全等问题。
• 📊 AI和ML的成功需要业务的大力支持，包括领导层的支持和资源分配。
• 🎯 产品管理者应关注AI和ML技术的最新发展，以便在产品中实施创新用例。

Q & A

为什么产品经理需要学习人工智能和机器学习？

产品经理学习人工智能和机器学习可以帮助他们掌握新技术，最大化客户和用户体验，提供差异化和有吸引力的AI驱动功能，从而使组织保持竞争力，避免被更敏捷的初创公司颠覆，并推动最大的收入回流到组织。

如何将AI集成到现有产品和组织中？

可以通过与云服务提供商合作，利用他们的API和AI能力来增强自己的产品功能。对于大型组织，可以利用已有的AI技术平台和产品团队。对于早期AI应用，建议从云服务提供商开始，而不是从头构建模型。

人工智能和机器学习的关键能力包括哪些？

关键能力包括回归、分类、神经网络、强化学习、自然语言处理和计算机视觉等。这些能力可以用于预测分析、用户旅程中的决策支持、文本和图像生成、语音识别和内容提取等。

如何开始AI和ML的创新之旅？

作为产品经理，可以从识别用户痛点和旅程中的AI应用开始，然后通过概念验证和用户验证来测试和验证想法。最后，将这些功能产品化并推向市场。

在实施AI技术时，产品经理可能会遇到哪些法律风险？

可能的法律风险包括数据所有权问题、客户数据训练的合法性、第三方云AI服务提供商的数据安全和隐私问题，以及确保遵守相关的数据保护法规。

如何确保AI模型的训练数据质量？

需要对训练数据进行适当的预处理，去除个人身份信息（PII），确保数据安全，避免使用有偏见或非法获取的数据集，并确保法律要求得到满足。

产品经理如何识别AI在其产品中的应用机会？

产品经理应该了解自己的产品领域、用户以及他们试图解决的用例。然后，可以根据这些信息思考AI和ML在现有产品和业务流程中可能发挥作用的地方。

什么是神经网络，它如何工作？

神经网络是一种模仿人类大脑神经元功能的机器学习模型，由多层节点（神经元）组成，通过权重和激活函数处理输入数据，进行决策和分类。

生成性AI技术有哪些应用？

生成性AI技术可以用于文本生成、图像生成、音乐创作等领域。它们通过预测下一个最高概率的单词或图像来生成新的内容。

产品经理如何使用AI技术提高自己的工作效率？

产品经理可以使用AI技术来生成产品文档的大纲、测试用例列表、研究问题、路线图项目和功能等。此外，AI还可以用于生成演示数据和进行市场分析。

Outlines

00:00

🤖 人工智能与产品管理概述

本段落介绍了人工智能和机器学习对于产品经理的重要性，强调了学习AI的必要性，并概述了AI如何融入现有产品和组织中。提到了AI的能力以及可用的工具，并以简单的例子解释了AI的工作原理，特别是监督学习、非监督学习和神经网络。强调了产品经理需要了解新技术，以最大化客户和用户体验的价值，并帮助组织保持市场竞争力。

05:02

🚀 创新与产品管理中的AI应用

这一部分讨论了为什么产品经理应该学习AI和ML，并探讨了AI在产品管理中的应用。强调了创新的重要性，提到了Peter Drucker的七大创新力量，并讨论了AI如何提供额外的价值和差异化。同时，也提到了组织如何通过云供应商来实施和采用AI技术，并建议产品经理从了解组织内部已有的AI技术开始。

10:02

🛠️ 组织结构与AI实施

本段内容讨论了组织结构在设计AI和ML能力时的重要性。对于小型组织和大型组织的不同情况，提出了集中化平台的概念，并讨论了产品团队如何与中央团队合作。还提到了产品管理过程中的创新旅程，包括创意、概念验证和产品化。

15:03

💡 AI技术的实际应用

这一部分深入探讨了AI技术的实际应用，包括如何开始创新旅程、组织结构的设计、AI实施的技术方法和组织结构。讨论了如何通过云供应商来增强产品能力，以及如何利用现有的AI技术进行投资。同时，也提到了在产品管理过程中，如何通过理解用户需求和用例来寻找AI的应用机会。

20:05

🧠 探索AI的工作原理

本段内容深入探讨了AI的工作原理，包括监督学习、非监督学习和强化学习等不同类型的机器学习。通过具体的例子，如篮球投篮预测和神经网络分类，解释了这些算法是如何工作的。强调了产品经理需要理解AI的基本概念，以便更好地应用到产品中。

25:05

🌐 利用AI进行文本和图像生成

这一部分讨论了AI在文本和图像生成方面的最新进展，如GPT和Dali等生成模型。解释了AI如何通过预测下一个最可能的单词或生成图像来工作。同时，也提到了产品经理如何利用这些技术来提高工作效率，例如通过AI生成产品文档、测试用例和研究问题等。

30:07

📚 学习资源与未来展望

最后一部分提供了一系列的学习资源，包括在线课程、新闻通讯、YouTube频道和报告等，以帮助产品经理深入了解AI和ML。同时，也强调了产品经理在考虑将AI集成到产品中时需要考虑的因素，如数据收集、法律合规性和用户许可等。最后，鼓励产品经理学习AI和ML，以应对市场的挑战和机遇。

Keywords

💡人工智能

人工智能是指由人造系统所表现出来的智能，这些系统能够像人一样进行学习、判断和决策。在视频中，人工智能是核心主题，讨论了其在产品管理中的应用，如何通过AI提高产品价值和竞争力。

💡机器学习

机器学习是人工智能的一个分支，它使计算机系统能够通过数据和算法自动学习和改进。在视频中，机器学习被用来解释AI如何从经验中学习并做出预测或决策。

💡产品管理

产品管理是指指导产品从概念化到市场推出的整个过程的一系列活动。视频中强调了产品管理者学习AI和机器学习的重要性，以便更好地利用这些技术来提升产品竞争力。

💡创新

创新是指引入新思想、产品或方法的过程。视频中讨论了产品管理者如何利用AI和机器学习来推动产品和行业的创新，以及如何通过技术创新来应对市场竞争。

💡监督学习

监督学习是一种机器学习方法，其中模型从标记的训练数据中学习，以便预测输出。视频中通过篮球投篮的例子解释了如何使用监督学习来预测所需的力量。

💡非监督学习

非监督学习是机器学习的一种类型，它涉及在没有标记响应的情况下分析数据，以发现隐藏的模式或数据结构。视频提到非监督学习可以用来识别数据中的群组或模式。

💡强化学习

强化学习是一种机器学习范式，其中算法通过与环境的交互来学习，以达到某个目标。它通过奖励或惩罚来调整策略。视频通过机器人把圆柱体放入圆圈的例子解释了强化学习的概念。

💡神经网络

神经网络是一种模仿人脑神经元结构的机器学习模型，由多层神经元组成，可以处理复杂的数据输入。视频中提到神经网络用于图像识别和文本分类等任务。

💡自然语言处理

自然语言处理是人工智能的一个分支，它使计算机能够理解和生成人类语言。视频中提到了自然语言处理在语音识别和文本生成等方面的应用。

💡计算机视觉

计算机视觉是使计算机能够从图像或多维数据中识别和处理视觉信息的技术。视频中提到计算机视觉在自动驾驶汽车和图像识别等领域的应用。

💡生成式AI

生成式AI是指能够创造新内容的人工智能系统，如文本、图像或音乐。视频中提到了生成式AI在文本生成和图像生成方面的应用。

Highlights

为什么产品经理应该学习人工智能和机器学习

如何在现有产品和组织中实现AI

人工智能的组成能力及可用工具

理解AI黑箱中的监督学习、无监督学习和神经网络是如何工作的

作为产品经理，如何通过AI和ML提高用户体验和产品价值

创新团队和新产品零到一市场推广的经验分享

如何识别并利用AI和ML在产品和行业中的用例

AI成熟度初期，推荐使用云供应商AI而非从头开始构建模型

在大型组织中，AI产品领导如何与中央团队合作

AI技术策略、组织结构和执行过程的考虑因素

AI和ML在产品管理过程中的风险和挑战

AI成功需要业务的大力支持和执行层面的支持

AI和ML技术的最新进展，如计算性能、存储能力和成本效益

如何使用AI进行文本生成和图像识别

AI在产品管理中的实用工具和资源推荐

Transcripts

hello and welcome to today's

presentation on artificial intelligence

and machine learning fundamentals for

product managers my name is Darris

Kumari and I'm a director of product

management at a Fortune 500 Enterprise

SAS tech company and I've had the

experience of leading both Innovation

teams as well as net new products zero

to one in the market that take advantage

of AIML capabilities

in today's presentation I will cover

simple topics such as why you should

learn AI as a product manager how you

might operationalize AI within your

existing products and organization the

capabilities that make up artificial

intelligence or the tools that are going

to be at your disposal and then finally

simple examples and to actually

understanding what goes inside the black

box of AI how does supervised

unsupervised and neural Nets work in the

AI landscape

as a product manager it is key to enable

yourself on new technologies like Ai and

ml in order to maximize your own

customers and your own users experience

and the value they get out of your

products to help your organization stay

competitive in the market and not face

disruption by smaller startups with more

agility and importantly help Drive

maximum Revenue back into your

organization by offering differentiated

and compelling AI driven capabilities

beginning with the topic of why to learn

AIML as a product manager it's important

to highlight the benefits of innovation

now you may be familiar with Peter

drucker's seven key forces of innovation

that can drive opportunities for

Innovation but specifically to AIML

you're going to find improvements in

process needs industry and Market

changes new knowledge that has

formulated into these algorithms and

potential incongruities with user

expectations

to simplify the explanation AIML will

allow you to unlock a different

capability or experience that'll provide

additional value to your customers

employees and other users to allow your

organization to get premium Revenue

capture and product differentiation this

is extremely critical to the longevity

not only of your product but to your

larger organization to continue to

innovate onto its core competencies

now if we overlay this conversation of

innervation on top of a popular book and

concept of the innovator's Dilemma it

goes to tell the following story which

is that in many cases your existing

organization the existing product you

have may not have AIML capabilities and

that's because you've rightfully been

focusing on your customers demands and

the requirements of the market where

they wear today and you've gained a lot

of market share as a result what ends up

happening though is that the pace of

Academia the pace of what's potentially

possible through technological growth

and in this case what Ai and ml can do

with the latest trends in gen Ai and

other such Technologies is you see the

potential exceeding or performance

oversupply the demand of the market and

what ends up happening is smaller

startups that are a bit more agile that

are focusing on more of this bleeding

edge technology coming out of Academia

and doing research they're able to take

this new technology to Market productize

it and eventually match the demand of

the market because the expectation is

we're seeing today of AIML is no longer

something that's future facing it's

something that's becoming table Stakes

for every product manager to add into

their products to stay competitive and

to stay relevant and so the challenge to

you is how are you able to add your own

knowledge of AIML and the own use cases

to your domain to your industry to

identify opportunities to shift your own

organization right on this Innovation

scale

and so now that we have a good

understanding of the importance of AIML

as it goes to your own organization's

Innovation strategy let's talk about how

your organization can actually Implement

and adopt AIML technology starting with

as this is a technology the landscape of

where to invest as with any technology

or product problem and solution you'll

have a build by or partner opportunity

available to you and what we've seen out

there in the market is it's very hard to

build an entire AIML competency in-house

it's expensive to not only hire the

talent required for data science

resources but also there's a significant

infrastructure cost from a capex

perspective to get the necessary servers

for the training and for the investment

and as a result what we're recommending

what we've seen in the market is what

we're calling the cloud vendor AI so

these are your big players like

Microsoft with what they're doing with

Azure with Google with Amazon on with

IBM they're offering these API inference

calls as essentially

transactions and so your organization

simply needs to identify where in the

workflow you need what AI capability

identify the vendor that has that

capability send the API call in and take

the result and so you're essentially

augmenting your own product capabilities

with these third-party Cloud vendor AIS

to add that AI capability now if you're

in a larger organization or maybe your

organization already has proprietary

in-house AI technology built on its own

platform and made available to its own

product teams that's fantastic that's

the first place to start as a product

manager is learning about what is

available already in my organization and

what technologies can I re-leverage that

maybe other teams have already

implemented

but that's really the the playing space

where I'd recommend making AI technology

Investments it's a bit less desirable to

try to build your own models from

complete scratch using something like

open source libraries it's more

recommended even to reuse something like

from a hugging face something that's

been published that's open source but

what I'm hiding is building something

from scratch isn't the best opportunity

especially if you're early on in your

AIML maturity and so to recap you can

begin your Innovation Journey as a

product manager down AIML capabilities

not requiring necessarily any AI

technology in-house but beginning at

looking at vendors Cloud vendors for

opportunities to partner

now

that we've touched briefly on your

technology approach it's important also

to look at Classic organizational

structures when designing how to

actually scale out and building your

organization for AIML capabilities now

if you're a smaller organization maybe

you're a single product manager and just

one product team with a few products

under you you may not have this problem

of scale and in which case you're going

to be working directly as a single scrum

team or a set of scrum teams on your

product

looking towards Innovation and we'll

touch on that in a moment but generally

what we see in large organizations is

there's going to be a centralized

platform body that is providing the AI

essentially as a service to other

product teams within the organizations

you could call this The Hub and then out

in your different business units or the

different domains of your product

applications the different lines of

business you may have ai product leads

that are interfacing with that Central

team with either requirements but more

importantly just with a knowledge

expertise from a product owner

perspective on the domain as well as the

AI capabilities and in application

development perspective from

architecture and implementation and

there you'll be able to drive the key

requirements roadmap and vision that

ultimately get executed on by each

individual scrum team that might be

supporting your product again it could

be one team it could be multiple teams

but take uh take a moment to pause the

video if you'd like to ingest what's on

the slide if organizational structure is

something of Interest otherwise we're

going to dive forward and actually talk

to the actual journey of thinking about

as a product manager where do I even

begin now that I know maybe I'll use a

third-party technology maybe there's

something in-house for AI so what's next

for my product

and so for that I definitely recommend a

split approach in terms of ideation

proof of Concepts and actual

productization to get a AIML capability

out to Market

and what I mean by that is as with uh

really any robust product management

process you should always start with the

pain points you're looking to solve

start with the user Journal you have in

mind and ask yourself where in my

product where in my user Journey today

is there any form of Intelligence coming

into play it could be a systems already

getting a decision for your users it

could be a users in the loop calculating

something or identifying something or

making a decision or even following a

specific business process as part of

that product or maybe consuming

something in it as a user and more of a

b2c concept the idea is AI is going to

be able to improve and augment those

existing processes and capabilities to

proactively serve your users insights

serve your users informations make

decisions take actions on their behalf

but it all begins with understanding

your domain your user and the use cases

you're looking to solve for and we'll

get into what AI capabilities you could

think about in a bit later in today's

presentation

but fundamentally as I highlighted it

begins with you as the product manager

thinking about where in the process

today AIML can fit in to essentially

improve and augment an existing

decision or intelligence uh step within

a process product and capability

and that's where you'll be able to take

that requirement in more of a proof of

concept hackathon informal fashion go

work with your scrum team to actually

balance out a little lightweight proof

of concept of

can this idea that the product manager

brought to us on implementing AI can it

even work am I getting the results I'm

expecting is the user experience

something that's explainable and it

makes sense and so before you even think

about productizing something actually

bringing it to the market to your users

is tremendously valuable to test it in a

proof of concept and even if you have

some customers available to either in a

design partner manner or just some close

customer contacts you've worked with as

a product manager it's a great

opportunity to take that proof of

concept and to do additional validation

that this isn't just using AI to use AI

which is something I'm going to call out

AI is just another tool in your

capabilities not every problem not every

product will need AI necessarily now ai

could become part of other points in the

user experience interfacing with your

product maybe to purchase it maybe on

the support they receive but keep in

mind your product itself you as a

product manager will be best equipped to

identify and understand is there

opportunities for AI in my product

and so later in today's presentation

hopefully we'll share some ideas that

will help you as a product manager

Identify some thoughts and capabilities

add to your toolbox on what AIML can do

and so once you've done the ideation

your team has done the POC you've

validated uh technically and from a

business value perspective that it is

improving the user value then you can

actually launch and productize and as

you go to productizing an AI capability

I will say there's a bit of red tape you

might run into as a product manager

there is the legal aspect who owns the

data can you train on your customers

data where's the data coming from even

if you're using a third-party Cloud AI

are you allowed to send your customers

data to that third-party for processing

so you might have some contractual

updates and of course on an I.T side if

you're doing it in-house you may need to

make sure that your it infrastructure

your data center is equipped for the

right server types to do the expensive

training processes not as much of a

concern if you're using Cloud AI

so by taking into account your AI

technology strategy the organizational

structure and your execution process you

can begin envisioning your strategic

approach to AI which is traditionally

split into three phases first you could

think of it in from a systems

architecture and engineering approach in

terms of how are you going to get your

data what are the algorithms you're

going to use where is the human machine

teaming going to come into play is your

compute in place for storage and what's

the governance like for robust and

responsible Ai and from there you can

Define the Strategic principles that

will educate that around the vision for

your product as a product manager

incorporating Ai and what that ideal

state looks like the governance culture

infrastructure that needs to be in place

to accomplish that Vision as a product

manager and the AI Talent you may need

on your team to help execute against

this again if you use cloud vendor AI

not so important to have the talent the

key thing is you as a product manager

being aware of the technology in use

cases you can implement which brings us

to part three so you've thought about

your persona you've thought about the

key pain points in use cases and where

to apply AI what use cases do you Target

first and for there there's a very

simple diagram that can be used for

human machine augmentation which is

taking your highest confidence decision

with the lowest risk and Outsourcing

those to the machines first and that's

where we can talk about the reimagining

of work and the age of AI reimagining

how your product works and the processes

your products to support where they'll

still be human only activities around

leading empathizing creating and judging

but you'll start seeing these human

machine teams and compliments around

training explaining sustaining

amplifying interacting and embodying and

machine only interactions where

Automation and AI will actually take

over a lot of that human workload around

simple transactions iterations

predictions and evolving and adapting

and actually learning based on an

environment and so AI in the short term

and for simple use cases very very well

suited for delivering recommendations

and insights in your products and humans

May remain in the loop to make judgment

and decision based on that information

now with any new technology and new

technical process you will run into

risks and AI of course comes with its

own set of risks so as you go about

ideating and potentially launching proof

of concepts of AI capabilities there's a

couple key areas to look for for risk

and to mitigate risk one is of course

you need data to train your AI models or

data to even send in to get a result

from a response from an AI endpoint and

so you need to make sure that you

properly pre-process your data strip out

pii information if you're housing that

data keeping your customer data safe and

making sure there isn't any biased data

use in your data sets illegal data

acquisition make sure the legal

requirements are in place and make sure

the infrastructure is both secure and

scalable enough to house the training

data and to train on the data as

necessary

as you go into building testing and

deploying make sure that there is Gates

and checks and you have an ability

organizationally to disable capability

if required because there may be for

self-improving algorithms uh there may

be a risk of ml poisoning there may be

vendor risk where if you're deploying uh

depending on a third-party Cloud vendor

that vendor goes down for a period of

time what's the impact on the user

experience is there a fallback for a

non-ai capability making sure your

product can handle that and then finally

having a feedback loop in and this could

just be on within the product collecting

some csat data from more of a plg

perspective but also collecting feedback

on the performance from a Telemetry

perspective of your models how many

times are users taking recommendations

how many times are users changing data

after AI changed it so you know it's

been a uh inaccurate prediction or value

and so having your own Telemetry as well

on the performance of your product will

be key as you go into implementation and

so up to date we've discussed kind of

the benefits of innovating with AI we've

discussed some organizational structure

we've discussed a little bit on how to

think about structuring the AI process

beginning as a product manager thinking

about use cases pain points going into

pocs validating customers and then

finally We Touch briefly on these

technology risks and the last thing I'm

going to highlight is AI to be

successful

requires significant support from the

business as well I'm sure all of you as

product managers AI in your backlog

might be something that historically had

been more future facing I mean today

with the hype around chat gbt and gen AI

has discussed and maybe something your

leadership is asking you for today and

so the biggest problem in the past was

lack of executive support we're seeing a

lot of that happening now in

organizations they're getting the

executive support they need from the CPO

from their own customer demands giving

them the funding and resourcing they

need to Pilot and test those pocs around

AIML capability

and so I'm very excited to Now cover

what are the capabilities to think about

as a product manager the new tools if

you will in your toolkit to think about

actually implementing in your product in

those pocs whether it's in-house or

whether it's using a cloud vendor Ai and

first of all before we do so it's

important to highlight that the reason

we have so much AIML power available to

us today is significant strides on both

compute in terms of the performance to

compute the amount of storage we're able

to have the models we can train and then

finally the cost has come down to a

point where it's very very uh efficient

and available to organizations to uptake

from a vendor perspective and so

artificial intelligence as we dive into

the capabilities here is it's a very

large State they call it suitcase term

you know a broad field of study a lot of

subsections really working to make the

the systems and machines perform human

tasks and so in this diagram here I've

listed out some of the key types of AI

that you're going to run into and you're

going to hear in the market and as a

product manager and it's important to

highlight that there's a lot of

different capabilities related to what

humans can do today with their

intelligence and their reasoning that we

can now have these systems in many cases

outperform Us in in conducting and so

the basic machine learning could be

something like regression which is

calculating given an input what a given

kind of output is going to be a more of

a data set you know a continuous data

set you have classification which is

more discrete you're trying to identify

something in fix buckets it could be

identifying a color identifying a shape

identifying a category so fixed classes

and then you have the neural next and

we're going to get into neural networks

and how they work at the end of today's

presentation but that's the capability

that's given us powerful text and image

generation such as the chat gbt you know

from open AI or Dali and that's type of

models and you also get things like

reinforcement learning you know when

you're seeing the robots that are able

to walk themselves like happening at

Boston Dynamics it's a great example of

reinforcement learning where the systems

are given rewards you know they have to

achieve a certain outcome and they're

given parameters that they can

manipulate to try to get that outcome

and then you have of course a within

those flavors of supervise just meaning

you have structured data structured

inputs and outputs and unsupervised

learning where it's a bit more

unstructured there's a lot of data and

the AI is essentially telling you the

pattern telling you the Insight in terms

of that data very popular for things

like clustering or finding groups

now there's also simpler things like

simple decision trees you know I think

your call center it's not high-tech

technology but it's technically an

artificial intelligence making decisions

and then you have as an extension of

that call center technology and

basically just speech and communication

the whole speech and text branches of

natural language processing natural

language generation natural language

understanding intent entity extraction

and in all the capabilities that are

famous with things like Siri and Alexa

around speech to text text to speech

translation content extraction and even

summarization which are some of those

use cases that you're going to get there

and then Vision you know it's

self-driving cars very popular

pioneering work done by the team over at

Tesla but a lot of organizations now in

the space but there's the need for

computer vision and all the other

applications on what you can do with uh

identifying footage whether it could be

geospace information whether it's camera

information from a security perspective

and image recognition and so maybe

you're getting a stop sign getting the

speed limit or so forth and so more on

the OCR side

but fundamentally these are a number

this is just a small list by by all

means there's a lot of additional

contacts especially as you get into

things like Robotics and other domains

there's a lot of extensions deep

learning and so forth Beyond these

simple Concepts but for most product

managers and especially for those that

are just beginning their Journey on AIML

Concepts this is a relatively good high

level view that I found to introduce

some of the capabilities to think about

may be part of your user Journey from

activities or inputs outputs they're

expecting in their user experience

now that we have a shared sense of some

of those basic capabilities on AI and ml

it's time to look behind the covers and

see what's actually going on in the

black box so you have a bit more of an

appreciation for how these algorithms

work at the end of the day it's math not

Magic

so let's take a look at some of these

simple Concepts and to remind you

machine learning can be defined as a

study of computer algorithms that allows

computer programs to automatically

improve through experience so there's a

concept of improvement there's a concept

of it figuring out a solution on its own

without having every case every

permutation coded ahead of time and we

talked about a few of the use cases in

the capabilities slide but other

examples include identifying people and

self-driving cars identifying emails if

they're spam or not predicting home

prices and temperatures teaching robots

to move and sort uh packages as they do

at Amazon and play games and understand

and come up with text images and sounds

a lot of the deep fakes you see or the

image and text generation you see today

now let's begin though getting into a

little bit more of the details around

some of these common types of AI and ml

beginning with what we called previously

supervised machine learning now when you

hear supervised machine learning it's

important to highlight that we're really

just High uh explaining that there's

labeled data involved and you're looking

to predict or map a given set of inputs

or features as they're called to a

corresponding set of outputs and

something important to highlight here is

there's generally a correct output or a

correct answer for a given input and so

given the number of Dimensions you have

you're going to have a line essentially

driven to make decisions but in a simple

two-dimensional example here we could

look at classification it's drawing a

decision boundary and you could see two

classes then being allocated to these

points on the graph depending on where

they lie in the boundary or a regression

based model which is more for that

continuous data set so predicting your

home prices versus predicting your

temperature there you may have an input

of how big is it you know what's the

square footage and then your output of

the price and that's again

plotted on that line as the prediction

and so supervised learning is all about

having that labeled data and labeled

data set whether it's hey all these

pictures or cats all these are dogs or

the attributes hey all these

temperatures correspond to the following

cities on the following days and so

forth regression but label data is the

key piece here for supervised machine

learning

now the next type of classic machine

learning is what you'll call

unsupervised machine learning so whereas

with the supervising machine learning

you kind of had labeled data set you

knew what the input was and what the

output was on those examples with

unsupervised machine learning you're

just feeding a lot of data into this

system here and then it's coming back

with what it detects as generally

clusters or patterns commonalities and

so maybe it's doing it based on the

color and it's giving you a grouping on

color maybe it's doing it based on the

height maybe it's doing it based on the

number of edges or the shapes and so

here there isn't one right answer right

there's in a fixed output to get based

on the inputs they're looking for it is

more of an observation that you're

predicting on and so clustering is a

very very popular case here where you

take in a lot of raw data and maybe you

want to assign some kind of a label or

attribute based on a cluster

and so that's uh the second piece of our

machine learning puzzle we had the

supervise labeled done supervised

unlabeled

now the next common type of machine

learning is something very cool it

referred to as reinforcement learning

now the unique thing about reinforcement

learning is that what you're defining

ahead of time is really just the

objective of the AI what they call an

objective function which is the goal and

then the AI is learning through multiple

simulations and this doesn't have to be

in the real world generally you could

use software to do the simulations and

it could do simulations in parallel to

learn from it but it does these

simulations and sees given this fixed

set of inputs and the outputs that

happen as a result of those inputs is it

getting closer to this objective

function scoring higher so to speak and

it's using that to guide its decision

making and so in this video example you

could see a robot is trying to get that

green circle into the red circle and the

graph you see in the lower right is the

result of the objective function the

closer it gets into that Circle the

higher the score of the objectives uh

function and so

the robot is essentially learning how to

move the green cylinder into the red

circle because it's maximizing the

reward it gets based on how it moves its

arm and how it moves this green circle

in space relative to that red circle and

so there's a lot of cool examples about

this and papers published on this online

around AI agents playing hide and seek

learning to walk learning to navigate

obstacles and it's a very cool example

of machine learning

solidify some of these Concepts what

we're going to do now is walk through

two

size machine learning examples in

Greater detail so you can understand how

the algorithms work and generally what's

going on in those black boxes of all

those AI capabilities we listed earlier

we're going to look at two examples one

prediction and one classification for

the prediction example we're going to be

given a certain set of data and we're

going to understand a measure of

correctness which is going to be making

a basketball in the hoop and we're going

to be able to train a prediction

function to say given wherever our

individual or in this case this robot is

in space we're going to be able to

predict the power and angle which it

needs to throw the ball in order to make

the hoop and so that's going to just use

a simple linear regression on the other

hand we're going to get a bit more

complex by looking at how we can do

classification however this time using

the concept of a neural network and

neural Nets or deep learning Nets as

well as multiple layers that's something

we're going to spend a lot more time on

towards the end of today's session

because it's an extremely important and

Powerful concept that powers a lot of

modern AI

so let's walk through this first

prediction example of using linear

regression to identify how to throw a

basketball given any position on the

court

and so given this example we essentially

have one simple input and one output the

input is the distance away from the

basketball hoop

and the output that we're after is how

much force they should use to throw it

now we're going to assume that their

angle is going to be fixed for this just

to normalize uh on a single Dimension

here what we're predicting and what our

input variable is but keep in mind in

practice you could have multiple

variables all happening at once

now the goal is to predict of course

what force we need to use to actually

get that ball into the hoop and so when

the algorithm is just starting out

without any data it has no idea how hard

it needs to throw the ball to actually

achieve the goal of making it in the

hoop

and so what you need to do is collect a

ton of data points to understand what

that success looks like and if you have

the data already it's a matter of just

labeling and collecting the ones that

were successful so you can see here this

is actually the balls turn green anytime

they go in the hoop indicating a

successful data point but otherwise

you're going to need to go out and

actually collect this data so you have

those sample examples of success and

linear regression as an algorithm is a

simple y equals MX plus b line and so

it's great for 2D representations of a

single input a single output but as I

highlighted you could get into

quadratics and multiple variables with

actual applications of this similar

technology

in our example though we're looking to

collect the training data of our shots

that went in and the algorithm is going

to then be trained to learn off of what

that success looks like

so what we'll be able to see here is

that we can plot out all the rows of

these data points where you have a given

X distance and a given y uh force that

you need to use based on that distance

and a simpler linear regression just

calculates the optimal y equals MX plus

b line minimizing the squared error

which is essentially the distance any

point is away from the line so how far

off was your prediction and so it

calculates that line that minimizes the

distance any point is away from the line

on average and so we reduce that error

rate in most machine learning algorithms

have a concept of an error rate that

you're looking to minimize to get an

optimal line and while in this case

we're very uh sure that it's going to be

a comprehensive data set of different

points and the Court's not going to

change the hoop height is not going to

change it's important to be mindful of

this concept of overfitting so in the

real world not all environments are the

same and so this model trained for this

hoop at this height we could have a

model that's over fit for just this

scenario and doesn't take into account

say the player height or other Hoops or

the type of ball you're using and so on

and so forth and so overfitting just

means your line has been optimized for

your training data and the data you have

but not necessarily the real world

but in this example we're quite

confident and happy with the result of a

line that maps to the input force based

on the distance we have to get us a

successful hoop in the basket

now to get a bit more technical on this

whole concept of mapping a a set of data

against the minimal error rate it's

important to throw out a word out there

called gradient descent this is a very

very popular term and it essentially is

just a graph that highlights your error

rate at different values in your

algorithm and in the input so in this y

equals MX plus b we're testing different

M's we're testing different B's and

we're trying to figure out at what point

of each do I have a relative minimum or

the bottom of that gradient descent

array curve and so we can see here as

the line continues to fit closer and

closer to the points the error rate

continues to decrease closer and closer

to ideally zero so the lower the error

the better and that's what helps get us

our optimal y equals MX plus b line or

our optimal prediction based on any X or

position we are on the court what that y

or the forces we need to use to make

that basket

and just like that we have just seen how

we've created together an algorithm that

is successfully making any basket and

every basket no matter where it is out

there on the court and we did that

through a collecting the data be

labeling what success looks like and

then see training a model that reduces

our error rate and as we highlighted

this was a very very simple the simplest

example using just a two-dimensional

space y equals MX plus b in the real

world you have different inputs which I

apply as different dimensions and

different variables and so instead of

just a simple linear function you get

into quadratic or logistic or other

function types based on the environment

and the data

now where something like linear

regression really pulls a lot of its

source from statistics the next example

we had of classification utilizing a

neural network is taking more from

biology and this is extremely

fascinating if you haven't learned about

this a neural network is a way to teach

the computer how to process the data

mimicking the way the human's brains

neurons function in our own biological

processing it's really a type of machine

learning that sometimes also they call

it deep learning because it's multiple

layers of neurons that fire in a in a

sequence to interconnected nodes that

they call neurons with different

activator functions and we'll get into

all this in detail in the upcoming

slides but the takeaway is it's actually

built to replicate in a way the human

mind and human intelligence using these

artificial neurons

so let's take a look at how neural

networks work for classification

so let's take a deep dive into one of

these orange neurons in our neural net

what is a neuron really when it comes to

our mathematical algorithmic

representation that machine learning

uses really at the end of the day keep

in mind you have a series of inputs

coming into this decision maker of a

neuron which then spits out its outputs

to additional neurons or potentially to

your final output layer and so what the

neuron is doing is generally a it's

accepting inputs from other neurons or

from your raw input and it could be

applying weight to those different

inputs and that helps it understand

maybe importance of an input in its own

calculation

from there it generally has its own

summation function to take into account

all those weights with potentially a

bias function that offsets the summation

based on some type of an attribute and

from there we go into a very very

important step which is called an

activation function and the activation

function is useful because it helps

normalize

standardized and essentially keeps in

check the desired dimensions and traits

of the inputs to the next output or

prediction and so if you ever hear the

terms thrown around like sigmoid or relu

or softmax these are just different

activation functions that take all that

input data from your other neurons and

it makes a calculation based on it to

keep the output uniform or within some

type of a boundary

and so let's go into an example of the

classification let's say the difference

between a balloon and a tree how can a

model tell well you might use different

dimensions of decision making

one dimension let's say we just use

color okay well what if our balloon and

our tree are both the same color now we

need to add another dimension roundness

great now I have a green balloon and a

green tree but what if my tree was round

and so now we have to add another

dimension into our decision making which

it could be something like shininess and

so how these models work and in many

cases actually explainability is a

challenge in that you don't really know

what the model is looking at in terms of

these different dimensions but it's

fundamentally coming up with its own

decision making criteria in those

different layers in the neural net to

distinguish something like

is it a balloon or is it a tree and so

with enough Dimensions enough layers you

can accurately distinguish one object

from another and each Dimension is

essentially being checked the activation

function helping classify the object

based on those inputs it gets for each

of these different dimensions you know

different colors would have different

input values that then help us get to a

predicted output of is it a balloon or

is it a tree

now let's look at another classification

problem with a neural net and keep in

mind that a lot of the different

criteria that you use for decision

making would most likely be inputs that

are initially sent in to this neural

network model so say we wanted to

identify something as a dog or a cat

based on an image that came in first of

all computers can actually see anything

there's no images so there's a slight

asterisk here when I say we're looking

at an image of a dog really that image

would probably be turned into a matrix

of pixels and maybe the pixels are

assigned RGB values or other values

around their intensity or significance

and so what's happening in this case

with a neural net unlike our regression

example where you altered your M and

your B in that y equals MX plus b

formula with the neural net each layer

is playing around with the weights from

the previous layers it's playing around

with what it wants to assign

significance to and value to

until it gets to the next neuron in the

next layer and so

something to keep in mind as well is the

only thing you really have access to and

you see out of the black box is going to

be that output so while all those

neurons and and the weights and the

biases are being adjusted really all

you're seeing at the end of the day is

that final prediction or calculation

that output is it a dog or is it a cat

maybe in this case it's a probability

score of is it a dog uh or is it a cat

or something along those line that just

a class of dog or cat

and so extending on this cat and dog

image recognition problem and taking

into the context that your input layer

is feeding in the pixels of the image

you need to keep in mind that some of

the power of the neural net is the fact

that not all neurons will quote unquote

activate or send an additional input

into future neurons Downstream and the

reason that happens is through that

activation function we were talking

about earlier so you might have a set of

neurons higher up on a layer that feed

in given their weights into that

summation function but then that

individual neurons activation function

may decide if it's that one that zero or

what that value is that it's going to

continue to pass Downstream to further

layers and further neurons and so that's

how a lot of the decision making

actually gets made which is certain

signals so maybe it uses something like

the size of the animal or the teeth or

the shape to make a decision it'll start

embedding those as neurons either do or

do not fire to help it make a decision

down the stream

and so let's bring these classification

with neural net examples home with one

more flavor and explanation and so in

summary keep in mind that your inputs

into these neural net models they of

course have to be some type of

mathematical representation because the

computer itself doesn't quite understand

images but it can understand the data

and so an input image say of a cat or a

dog would most likely be of a certain

pixel's height and pixels width and what

generally happens is you translate that

into a matrix or a vector and the

different colors might have different

values to them and that's really the

basics also and basis of computer vision

is taking these different pictures and

translating them into these numerical

Matrix representations

and so when we talk about classifying

and deciding if something is a cat or a

dog we really talk about beginning with

that numerical representation of that

image and then feeding it into our given

uh neural net here and keep in mind the

number of inputs you could have can be

extremely large you see some neural Nets

with Millions uh if not even billions

for the case of chat GPT input

parameters that it then fires off into

its its neurons to get to that final

output and so

as we highlighted the images are pixel

values we're using mathematics in terms

of calculating between each of these

neurons here the weights between the

lines between one neuron and another the

biases on each neuron and then the

activation function being used in these

neurons to basically say am I a one or a

zero right am I activating or am I not

activating and that ultimately is going

to get us to that output of a dog and

what generally ends up happening is all

those different features that are used

for decision making they'll happen one

layer at a time so maybe you identify

the edges of a dog and then you're

getting a bit more granular into what

the edges are combining as and specific

features like ears and nose and teeth

until getting to that final output and

so

it's really a combination of multiple

inputs and a combination of multiple

layers that are activating to get us to

that given output

and the same technology keep in mind can

be used for things like uh image and

text recognition or or OCR of is it a

one is it a two is it a seven and so as

you could see through this diagram of an

example as the different numbers get

interpreted and keep in mind they would

be translated into a matrix of their

values you have different neurons that

are lighting up here in green because

the activation functions criteria has

been met and the different weight and

biases are highlighting what those

numbers are keep in mind in this

specific text example as well there

would have been a training set of

thousand excuse me hundreds of thousands

in this case even of images maybe

thousands of numbers hand drawn with

actual manual labeling being done saying

hey this is a one this is a two and

that's what's being fed into train the

model

and so really really powerful stuff

mimicking the human brain in this case

using a neural net to help classify

something into a value we saw a dog tree

cat uh number number different use cases

here

now I would be very remiss not to touch

on the latest hype of things like Chachi

PT Dali or Texas generation so we'll do

that briefly before ending today's

fundamental overview session so how do

AI systems generate text we just saw

something like predicting your Force for

basketball we saw a classifying uh image

as a dog or cat how does this all

translate into things like generating

text well the great news is more or less

a lot of the fundamentals you just

learned still apply here when it comes

to text we treat each different kind of

piece of text as something that's called

a token so the word my is a token

favorite is a token color is a token is

a token red is a token and the training

data for something like a text

generation model could be as large as

every piece of text available online and

in public uh scientific Publications and

how the model is actually training is

it's predicting the next token so given

a token or context of tokens what is the

highest probability of the next token so

in this example if I had said my

favorite the neural net is saying the

highest probability coming next is color

then the highest probability coming

after that is is then the highest

probability coming out of that is red so

when you get into these text generation

algorithms what's actually going on is

they're predicting token by token the

highest probability word it thinks is

happening next and so

training data you know is taking these

tokens taking all these text examples

running that neural network and then

getting at that prediction for text and

keep in mind what we're seeing in

industry is things like aging GPT Auto

GPT Lang chain where users are using

these models uh like a GPT but they're

extending capabilities Beyond simply

returning your text to actually do

things and take actions in the world

through those examples provided and so

as a product manager this is a very very

relevant area of generative AI where you

think about any UI your users have or

any process where there's an input in

this output or there's a recommendation

or help you'd like to provide them you

have this advantage of taking this

text-based processing and then how you

want to interpret that text you don't

have to leave it as text on the UI or

screen you can take a text result and

display it as a set of check marks

display it as a Playbook book display it

as a summary on the UI and so forth and

so a lot of powerful use cases coming

out of generative Ai and being able to

have the system come out with a text

result given a text input

and image generation is actually even

more interesting than what's going on

with those tokens and the trained

examples of text generation and

predicting the next token because what's

going on with this algorithm for image

generation it all comes back to of

course having trained data but what

they've actually done with image

Generations they start with a given

image say of this pyramid that's been

compressed and then they'll go ahead and

they'll continue to add noise to that

image and keep in mind an image at the

end of the day is of course just a

matrix that's represented as numbers but

they keep adding noise to that Matrix

until finally you have an extremely just

noisy image that to the human eye you

can actually really make it out or see

anything

how these generative image algorithms

work is they'll then given an input so

say you started with this say you know

two pyramids uh with a blue sky that was

your input and you provided that image

and so the system added noise what it

then does is it starts with an image of

complete noise and then given your input

it attempts to get back to that original

state by removing the noise or it's

called denoising and they use something

called unit for this and so maybe you

typed in give me a beach landscape and

so of course it'll have images it's

trained on of beaches of Landscapes that

it's added noise to and so that it

starts with complete noise and step by

step it iterates in this net neural net

and it attempts to remove the noise from

it to get back to that Source image and

in this case that's how it kind of

dreams up an image if you will because

it doesn't have that exact image ahead

of time it is is incrementally peeling

back the noise and coming up with what

it thinks the image should look like so

very very fascinating and it's ways you

can generate text generate image and of

course as we saw from the other AI

capabilities you can do things like

music you can do things like voice you

can do all kinds of prediction

algorithms whether it's text or images

or or categories or temperatures or

prices and so a lot of opportunities for

your product as a product manager to add

capabilities in now on the note of

generative Ai and Technologies like

Chachi PT I will say not only can you

apply these Technologies within your

products you as a product manager can

apply these Technologies to make

yourself more productive and so examples

that I've personally found uh use in as

a product manager includes writing

outlines or skeleton starting points for

your epics your product requirement docs

turning short descriptions of stories

into lists of test cases or splitting up

stories you give it into two different

stories if you have larger scope

defining the research questions you

should ask for your new product or your

new domain identifying roadmap items and

features and here's another great one

identifying use cases for new

technologies like AIML within your

product or industry if you don't know

where to start you've gained a lot of

information today but you don't know

where to start you can ask chat GPT hey

given the following industry uh

following product on my product manager

sure what type of capability should I

consider on my roadmap great starting

point here it's also useful for

generating realistic demo data for your

engineering teams your QE teams for

testing it can be used to identify jobs

to be done and personas it's been

tremendously powerful for us to come up

with research questions that you then

take into research exercises as

questions and then getting go to market

considerations a lot more prompts and

ideas that I'll put into the links or

that you can request access to this

PowerPoint uh just leave a comment

requesting access to this PowerPoint

it'll be including all the links here

that you can click today now other

interesting examples include as I

mentioned identifying use cases with AI

for your product or even explaining

Concepts about your products and there's

a lot of other Technologies I'll say

that are out there on the market today

across a number of different Tech Stacks

that get you optimization in your

workflow in your processes on how you

get work done using AI

now while today's overview was by all

means a very brief and a very quick look

at what it looks like to be an AI

product manager there's a number of very

very useful resources I'd like to leave

you all with and it covers the what the

why and the how of AI and ml for product

managers and really Business Leaders on

the side of the what I highly recommend

the Coursera AI for everyone course the

Deep learning.ai batch AI newsletters

the two minute papers YouTube channel

and Stanford's read the AI index report

which I recommend you read as action

items I recommend engaging in hackathons

with your team to incorporate cloud or

in-house AI capabilities to familiarize

yourself with internal process and

approved vendors if required in terms of

what it looks like to get an AIML

capability including legal including it

and any dependencies and of course

familiarize yourself with the internal

capabilities that might already be

in-house in your organization on your

platform in your company's toolkit that

have already been approved and used

Maybe by other product teams

once you've done that it's good to learn

the why of AI and ML and how it impacts

your business strategy MIT has a great

AI uh implication for business strategy

six-week virtual async course there's

also a AI strategy in roadmap systems

engineering approach to AI development

and deployment one week kind of live

full day course both of them relatively

cover the same content so I would say

pick one of those as your next step and

for those that are looking to get more

technical on the back end of how some of

these AI ml algorithms work I do

recommend the machine learning

specialization on Coursera co-produced

with Stanford it's a three-week online

virtual async it covers supervised

machine learning Advanced learning

algorithms and unsupervised learning

recommenders and reinforcement learning

there's also for the generative AI folk

all that hype there's a great Cloud

skills course that Google put out

available as well that one's free and

then you'll find two good books that

have been recommended in Industry

designing machine learning systems and

iterative process for production already

applications by chipwin and building

machine learning powered applications

going from idea to product by Emmanuel

Amazin and so a lot of additional

resources I'm leaving you with and again

if you're looking for the raw PowerPoint

do leave me a comment and I'll provide

you a link to a hosted uh document

and with that ladies and gentlemen it

takes us to to taste summary for our AI

product management fundamentals and I

want to leave you just with the

importance of learning as a product

manager Ai and ml capabilities to help

you come up with new Innovative use

cases protect from those startups that

are going to be attacking you with new

technology and to help provide greater

value and differentiation to your own

users to lead to Greater Revenue

recognition for your organization

some final thoughts as well include the

fact that not every problem in every

product truly requires AI it's it's

tremendously useful in a lot of

applications not all applications AI is

math at the end of the day it requires

data it's not magic and data is key to

effective Ai and so as you think about

long term adding in AI into your product

think first do you have the Telemetry do

you have ways you could collect data do

you have the legal in place to allow you

to use that data consume that data and

what's that strategy look like is it

going to be in-house AI or most likely

maybe a big cloud vendor Ai and you have

your customer permission to send their

data there and so a lot of opportunities

as product managers a very exciting time

and for especially even as users as

customers a lot of exciting time where

you're going to experience your own

products in the market and the demand of

the market evolved to incorporate a lot

more AI ml capabilities over the coming

years so with that I thank you all for

listening and I wish you the best of

luck in encompassing and learning more

about AIML into your own Industries and

products