Webinar: How to Be an AI Product Manager by Facebook AI Product Leader, Natalia Burina
Summary
TLDR本次演讲由Facebook的AI产品经理Natalia Barina主讲,她分享了AI产品管理的重要性和AI产品经理的角色。她强调AI在当今社会中无所不在,对决策产生深远影响。AI产品经理需要理解业务问题,制定战略和路线图,并确保AI产品的公平性、隐私性、透明度和可靠性。她还讨论了AI产品管理与传统软件产品管理的不同之处,包括处理AI的不确定性和建立实验文化。最后,她给出了成功作为AI产品经理的三个建议:讲好故事、制定清晰的计划和快速上手新工作。
Takeaways
- 🤖 AI产品管理的重要性日益凸显,因为AI正在以前所未有的规模做出关键决策。
- 🚀 AI产品是自动系统,通过从数据中学习来做出面向用户决策,机器学习使计算机系统通过现有数据训练获得学习能力。
- 🛠️ AI产品经理的角色是为公司最重要的商业问题构想和实现有影响力的AI解决方案。
- 🎯 识别AI可以解决的正确问题和优先级是AI产品经理的关键任务。
- 🔍 AI产品经理的类型包括专注于产品的、平台的、研究的以及专注于构建负责任AI的产品经理。
- 🌟 AI软件开发与传统软件开发最大的不同在于其概率性,机器学习将工程从确定性过程转变为概率性过程。
- 📈 AI产品经理需要掌握的实用技能包括使用创新工具、理解AI产品管道、以及关注业务目标和技术潜力。
- 🔧 构建负责任的AI意味着确保AI的公平性、隐私保护、可解释性、可追责性和可靠性。
- 📊 成功作为AI产品经理需要深入理解问题、内部化业务目标、对齐和跟踪正确的指标,并培养实验文化。
- 🚦 面对AI技术的强大力量,产品经理必须意识到伴随的责任,并始终以不伤害用户和客户为出发点。
- 🌐 作为AI产品经理,应与所有利益相关者合作,特别是领导层,以设计和对齐适当的指标。
Q & A
AI产品经理的主要职责是什么?
-AI产品经理的主要职责是设想和实现对公司有重大影响的AI解决方案,解决最重要的商业问题。他们需要首先理解AI能解决的最重要的商业问题,其次识别和优先处理这些问题,并制定愿景、策略和路线图。最后,他们需要确保这些解决方案得以实现。
AI产品与传统软件产品在产品管理上有何不同?
-AI产品的开发是概率性的,与传统的确定性过程不同。机器学习将工程从硬编码的算法和规则转变为基于大量输入输出对的统计学习过程。这意味着AI产品经理需要适应这种不确定性,并理解模型预测可能会有一定的错误率。
AI产品经理需要具备哪些技能?
-AI产品经理需要具备包括使用设计思维工具(如UX模型、线框图、用户调查等)在内的多种技能。他们需要理解AI产品管道,包括数据、模型、服务基础设施和实验等环节。此外,他们还需要理解机器学习可以解决的问题类型,如排名、推荐、分类、回归、聚类和异常检测等。
如何确保AI产品的公平性和无偏见?
-为确保AI产品的公平性和无偏见,产品经理需要设计和优化AI以避免内置的偏见。这意味着要确保AI的决策基于伦理和包容性原则,同时要意识到数据可能编码了人类和社会的偏见,并采取措施来减轻这些偏见的影响。
AI产品经理如何处理AI产品的隐私和安全性问题?
-AI产品经理需要在构建AI时保护用户隐私,并确保系统能够抵御攻击。他们需要考虑AI可能被欺骗的方式,并采取措施来增强系统的鲁棒性,以防止通过操纵输入数据来破坏算法。
AI产品经理如何确保AI产品的透明度和可解释性?
-AI产品经理应确保AI的决策过程是透明的,并且能够解释AI为何做出特定的决策。这对于高风险的使用案例尤其重要,因为它们可能会直接影响人们的生活。透明度可以通过提供决策背后的逻辑和数据支持来实现。
AI产品经理如何确保AI产品的问责性?
-AI产品经理需要建立检查和平衡机制,进行验证和合规性审查,确保有人类监督。这意味着AI决策过程不仅要自动化,还要能够在必要时进行人工审查和干预。
AI产品经理如何确保AI产品的可靠性和持续高性能?
-AI产品经理需要持续监控AI系统的性能,并及时检测和解决数据问题,以防止模型性能衰退。这包括对系统进行持续的优化和调整,确保AI产品能够持续提供高质量的服务。
AI产品经理如何衡量AI产品成功与否?
-AI产品经理需要与所有利益相关者,特别是领导层,合作设计和对齐适当的度量标准。AI应该支持业务度量标准,并展示其对业务的影响。成功的衡量可能包括早期的胜利、业务指标的提升以及与业务目标的一致性。
AI产品经理如何培养实验文化?
-AI产品经理应该帮助组织建立成功使用AI所需的文化。这包括鼓励智能风险的承担、从失败中学习并不断迭代。他们应该从简单开始,逐步获得早期胜利,并慢慢扩展AI产品的范围。
AI产品经理在开始新工作时应该采取哪些策略?
-AI产品经理在开始新工作时可以采用安德鲁·博斯沃斯的冷启动算法。这包括安排与关键人物的30分钟会议,介绍自己,提出必要的问题,记录详细的笔记,并询问下一步应该与谁交谈。这种方法有助于快速了解新环境并建立联系。
Outlines
🤖 人工智能产品经理的角色与挑战
本段落介绍了Natalia Barina作为Facebook的AI产品负责人的背景,并探讨了人工智能在当今社会中的重要性和影响。AI产品是自动系统,通过学习数据来做出面向用户决策的机器学习技术,使得计算机系统能够通过在现有数据上的训练来学习,并基于之前未见的数据做出预测或其他结果。Natalia强调了AI产品经理的职责,包括理解AI可以解决的重要商业问题、制定愿景和战略路线图,并确保AI产品的责任性、公平性、隐私性、可解释性和健壮性。
📊 机器学习与AI产品开发的实践技能
Natalia讨论了AI产品经理在实践中需要的技能,包括确定期望结果、交付方式和产品使用方式。她强调了在AI产品开发过程中理解数据、模型、服务基础设施和实验的重要性。Natalia还提到了AI产品生命周期,包括数据和训练、设计和开发、验证和测试、部署批准以及监控和优化。此外,她还提到了AI产品管理与传统软件产品管理的不同之处,特别是在机器学习的开发过程中的不确定性和概率性。
🛡️ 构建负责任的AI产品
在这一段中,Natalia强调了构建负责任的AI产品的重要性,包括确保AI的公平性、隐私保护、透明度、可解释性和健壮性。她提到了AI可能带来的潜在问题,如偏见、数据泄露和对抗性攻击,并强调了AI产品管理者需要理解这些风险并采取措施来最小化它们。她还提到了AI产品管理的一些关键领域,包括确保AI不带有偏见、保护用户隐私、提高AI的透明度和可解释性、确保AI的可追责性以及建立可靠的AI系统。
🚀 成功的AI产品经理的策略和建议
Natalia分享了成为成功的AI产品经理的策略和建议。她强调了识别正确问题和解决方案的重要性,以及深入理解AI可以解决的问题。她还提到了内部化商业目标、理解技术的能力和局限性、以及理解潜在的危害。Natalia提出了与所有利益相关者合作以设计和对齐适当的指标的重要性,并强调了建立实验文化的重要性。她还分享了三个产品管理的技巧:讲述好故事、制定书面计划以及快速启动新工作的策略。最后,Natalia以感谢听众的参与结束了她的演讲。
Mindmap
Keywords
💡人工智能产品经理
💡机器学习
💡深度学习
💡AI产品生命周期
💡公平性与偏见
💡隐私保护
💡可解释性
💡责任归属
💡可靠性
💡实验文化
💡业务指标
Highlights
AI is making consequential decisions at an unprecedented scale, becoming ubiquitous in various sectors.
AI products are automated systems that learn from data to make user-facing decisions.
Machine learning enables computer systems to learn from existing data and make predictions based on new data.
Examples of AI in everyday life include spam filtering, Google searches, and self-driving cars.
AI product managers (AIPMs) will be essential as AI becomes more prevalent in all technologies.
The role of an AI product manager is to envision and realize impactful AI solutions for business problems.
AI product managers come in different types, including product-specific, platform, research, and responsible AI focus.
AI software development is probabilistic, unlike traditional deterministic processes.
AI PMs must understand the AI product pipeline, which includes data, model, serving infrastructure, and continuous experimentation.
AI can cause unintended consequences if not carefully designed and optimized.
Responsible AI development involves ensuring fairness, privacy, robustness, explainability, and accountability.
AI product managers should focus on identifying the right problems and solutions, aligning with business objectives, and understanding potential harms.
AI should support business metrics and show its impact on the business.
Fostering an experimental culture is crucial for AI product managers to succeed.
AI projects can be difficult, and it's important to start simple, achieve early wins, and grow gradually.
Telling a good story, having a written plan, and using the cold start algorithm are tips for AI product managers.
Transcripts
good afternoon everyone
um
my name is natalia barina and i'm an ai
product lead at facebook
where i support a team of about 50
engineers pms and designers
i'm excited to bring you the event today
so let's talk about how to be an ai
product manager
why are we here today well ai is making
consequential decisions at an
unprecedented scale
in the last decade ai has become
ubiquitous
driving increasingly complex and
consequential decisions like credit
approvals college admissions courtroom
bail decisions etc
ai products are automated systems that
learn from data to make user facing
decisions
machine learning gives computer systems
the ability to learn by being trained on
existing data
after training the system can make
predictions or deliver other results
based on data it hasn't seen before
some examples of ai today that we don't
think about or we don't notice but spam
filtering in your email box
google searches
self-driving cars of course this one is
visible if you see them
driving around if you happen to be in an
area where they have them
um there's also systems that use deep
learning to infer how distracted or
tired drivers are
there's so many more
one of my favorites is the estimation of
house prices
because all technologies are touched by
ai soon every pm will be an aipm
this is really impossible to escape we
will all need to understand ai because
it's becoming so omnipresent
so let's talk about
what is the role of an ai product
manager
what is an ai product manager and what
do we do
well
an ai product manager
works to envision and realize impactful
ai solutions for companies most
important business problems
um
and the way to do this is to first and
foremost understand what are the most
important business problems that ai can
solve ai is not always appropriate in
all situations
secondly it's to identify
and
what are prioritize the right set of
problems and develop a vision strategy
and roadmap so this is really getting
into
what it is that you need to do
and then third is all product managers
our job is to make it happen
now there are different types of ai
product managers
um there are pr ai product managers who
work specifically on
products and need to have
um
a very
very strong understanding of what the
product does in order to use ai to
supercharge that specific product
um there are ai product managers who
work on a platform
so they work on developer tools and
infrastructure in order to build ai
there are ai product managers who work
on research so this is working with ai
researchers to bring research
breakthroughs to production
typically these are
product managers
in bigger companies where there's a
whole department that just focuses on
research
and then finally
there are uh product managers who focus
on building ai responsibly although
building ai responsibly has to be
everyone's responsibility but building
ari responsibly what does that mean
ensuring that ai is fair
private
robust explainable and then there's
accountability um
to people who use the ai
how is
product management
different
uh in the ai than for other um
for regular software
well
let's talk about this this is a really
interesting one
um
ai software developments
is
probabilistic
so this is the biggest difference
between traditional ai traditional and
ai tech products
machine learning shifts engineering from
a deterministic process to a
probabilistic ones
this means that instead of writing code
with hard-coded
hard-coded algorithms and rules they
always behave in a predictable manner
ml engineers collect a large number of
examples of input and output pairs and
use them as training data for for their
models
with machine learning
we often get a system that is
statistically more accurate than simpler
techniques but with a trade-off that
some small percentage of model
predictions must always be incorrect
sometimes in ways that are hard to
understand
[Music]
this is why it's it's sometimes hard for
for people to make that shift it's a
very different way of building products
let's talk about practical skills for ai
product managers
what is it that you need to uh
what kind of skills do you need to build
in order to become an ai product manager
well
as with um all all uh product managers a
pm must determine what is the desired
outcome how that outcome will be
delivered and how the product will be
used
before
starting the process of building which
every time you start it's a process
that's expensive
and it's an investment
so at the very beginning in the ideation
phase ai product managers should be able
to use the same rapid innovation tools
that
design experts use including ux mockups
wireframes user surveys
at this stage it's really critical to
frame the problem or the opportunity
that the product addresses
there's
sort of different
categories of problems that ml can um
can solve in their ranking
recommendation
classification regression clustering
anomaly detection etc
um it is imperative
that every ai pm should understand the
ai product pipeline and what does this
mean well you need
to build an ai product you need data you
need a model you need serving
infrastructure
and you have to experiment until ai
works and gives you the outcomes that
they're satisfactory
in reality there are many candidate
models
that are created during the development
process but basically the ai life cycle
is
this diagram i have here
data and training
design and development validation and
testing is important
approval to deploy and then finally you
have to have monitoring an optimization
to ensure that even when it's built and
it's out there in the wild things change
and so it's important to monitor the
system for quality
continuously
with great power comes great
responsibility so
ai supercharges technology products and
it's extremely powerful new technology
and here i'm borrowing from from
spiderman though this actually goes way
back to antiquity
um
we as ai product managers have to
understand that ai is so powerful
that we have to really think about the
responsibility that comes with
developing such products
in medicine there's the hippocratic
oaths stating first do no harm
likewise we as ai practitioners should
as a starting point not harm our users
and customers what do i mean by that
well
unless carefully designed and optimized
ai can cause unintended consequences
ai gets better
with more data but data encodes human
and societal biases
some examples are facial recognition
that cannot recognize darker skinned
faces
ai that inadvertently recommends high
paying jobs to men and low paying jobs
to women
etc there there are so so many ways that
ai can go wrong
let's get a little bit more specific
about areas for responsible ai
development and what you should think
about when you're building ai
oops
um so
first and foremost
by the way these are not in any
particular order so i shouldn't say
first and foremost but ai
you have to make sure that ai is fair
and it does not have bias so that it's
built in a way that's ethical and
inclusive
secondly it's important to preserve
people's privacy when you're building ai
and to secure secure against attacks um
there's some really interesting ways
that ai can be tricked
uh by in image recognition for example
where you just do a little bit of
manipulation
um
of an image and it completely can break
the algorithm so it's very important to
think about the adversarial aspects um
when building ai
ai should be transparent
so it should
we should be able to understand the why
behind the ai and why it makes certain
decisions
this is especially important when they
are
high stakes use cases that impact
people's lives
um
[Music]
excuse me all right
um ai must be accountable so there must
be checks and balances validation and
compliance
and be able to to uh really be
have human oversight and then finally ai
should be built in a way that is
reliable so you have uh continuous high
performance that the models don't decay
you detect data issues this is where
monitoring um in our pipeline up there
is really important
so how do you succeed as an aipm um
what are how do you um
how do you make this work it's hard
um
the most important thing is to be able
to identify the right problems and the
right solutions for them and the way to
do that is to deeply understand what are
the problems
um that ai can can solve
for for people who use it
secondly you have to internalize
business objectives um so
you have to understand what is
what is really the point of your
business and how you can support the
business with ai remember you have to
supercharge the business with
ai you have to understand what is the
technology what it can and can't do ai
is not suitable in all cases it is not a
silver bullet
and then as an ai product manager you
have to understand the potential harms
of this powerful technology
you have to align and track
align on and track the right metrics um
actually coming up with the right
metrics is not trivial it's oftentimes
difficult for a business to define and
agree on metrics
ai should support business metrics and
should show how
it makes an impact to the business
of course the worst case scenario is
that there aren't any business metrics
or that
sometimes what you see is there are
business metrics and then there are ai
metrics and they are in no way connected
and it's very difficult
uh
to to
to see
how they work with each other so
all ai should ladder up to the greater
uh business goals
and to get there you have to work with
all stakeholders but especially
leadership
to design and align on appropriate
metrics before building your ai products
without clarity and metrics it's
impossible to be successful and
how do you do this we could we could
have a whole session on this it is not
straightforward and it is highly
dependent on what kind of ai you're
building and what kind of a product you
are supporting
um
as an aipm it's really important to
foster an experimental culture so
part of an ai product manager's job is
helping the organization build the
culture it needs to succeed with ai
because it's so different from
traditional software development where
the risks are more or less well-known
and predictable
ai really rewards people and companies
that are willing to take intelligent
risks
um
and and for this you have to be willing
to fail everything will not work
but it is important to document the
lessons and to keep iterating
machine learning gives companies real
competitive advantages in prediction
planning sales in almost every aspect of
this business
however even simple machine learning
projects can be difficult and building
ai is much harder than most people
realize
um
there's there's some data to show from
from venture b uh venturebeat claims
that 87 of machine learning products
never make it into production
and harvard business review says the
first wave of corporate ai is bound to
fail so
machine learning
is not a silver bullet
the ability to make decisions based on
data is
a prerequisite for an experimental
culture
and measurement obsessed companies every
part of their product experience is
quantified and adjusted to optimize user
experience
um so you have to start simple get early
wins
wow i am getting
apology about that
um
start simple get early wins and slowly
grow your ai speed of products
as a conclusion i want to leave you with
my three favorite pm
pro tips
and they are telling a good story is
everything
having and telling a good story is the
most important thing you can do as a
leader
and this is
this is important for every pm
uh but it's especially important for
aipms because we have to make
people understand what it is that we do
oftentimes it's very technical it's hard
to explain
um
second
one of the things that i have been
implementing for some time in my career
is each half i have a written plan for
beginning
at each
beginning of each half and i have a
written plan for how next six months
will go
i share this with leadership and
colleagues and it really helps keep me
focused i never take meetings that don't
align with with my goals and
it's a it's a good way to
to really stay
balanced and make sure you're doing the
right things and then finally if you're
starting a new job one of the things i'd
recommend is
andrew bosworth's cold start algorithm
and this is a way that will help you
ramp up faster than you ever have before
and it's basically saying um if you
start a new job schedule
30 minute meetings with key people and
each 30-minute meeting should be
you should introduce yourself ask
everything you need to know
[Music]
um take copious notes and then ask who
else you should talk to it's pretty
simple but it's extremely effective
with that i thank you so much for your
time today i enjoyed giving this
presentation and sharing my experience
in ai product management
uh should you like to please reach out
to me on twitter this is my twitter
handle
thank you so much
[Music]
you
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