AI Product Manager
Summary
TLDR本视频由Dr. Bob主讲,深入探讨了人工智能产品经理的角色与职责。AI产品经理是领域专家,负责引导团队构建最佳的AI服务解决方案。与通用产品经理不同,AI产品经理需要对算法和技术有深入理解,并能识别哪些项目适合应用AI技术。此外,他们还需具备出色的沟通能力,确保利益相关者理解AI的潜力与局限,并指导团队进行正确的数据收集与分析。Dr. Bob还强调了持续学习和评估解决方案的重要性。
Takeaways
- 📚 AI产品经理是领域专家,负责引导团队构建最佳的AI服务解决方案。
- 🎓 该职位要求深入的专业知识,通常需要达到博士水平,尤其是在深度学习和机器学习方面。
- 🔍 AI产品经理需要了解不同的算法、技术和配置,以便为特定产品解决问题。
- 🚀 成功的AI项目可能会极大提升产品性能,例如谷歌翻译通过使用机器学习提高了翻译准确性。
- 🧠 作为专家,AI产品经理需要评估哪些项目值得投资,并准备好管理这些项目。
- 📊 必须确保使用的数据集正确,并选择合适的指标来评估AI的效果。
- 🛠️ AI产品经理应具备前瞻性,为团队和利益相关者提出最佳的AI实验和方法。
- 💡 组织培训课程,提高公司对AI的认识,理解AI只是一种工具而非万能解决方案。
- 🔄 每周评估现有解决方案,了解是否有更高效或更快速的方法,并保持对科学进展的了解。
- 🔍 评估新的算法和技术,判断其是否适用于产品,是否能带来更好的结果。
- 🌟 作为AI领域的专家,AI产品经理应不断提升自己的知识水平,确保在产品开发中发挥关键作用。
Q & A
AI产品经理与普通产品经理有何不同?
-AI产品经理是某一领域的专家,他们使用自己的专业知识指导团队构建最佳的AI服务解决方案。与普通产品经理相比,AI产品经理不仅需要具备产品经理、项目经理和技术领导的能力,还必须对AI领域有深入的理解和专业知识。
AI产品经理需要具备哪些专业知识?
-AI产品经理需要对不同的算法、技术和配置有深入的了解,能够根据特定问题选择最合适的解决方案,并具备将AI技术应用于产品以更好地服务客户和用户的能力。
AI产品经理在团队中扮演什么角色?
-AI产品经理通常领导一个实验团队,这个团队独立于常规的产品路线图,以便产品不必依赖于实验结果。他们需要具备专业知识来指导团队构建产品、收集数据,并做出决策。
AI产品经理如何评估团队的进展和AI使用的数据集?
-AI产品经理需要定义和选择用于评估数据集的指标,确保数据集的正确性,并建立警报系统以避免AI输出错误。
AI产品经理如何与利益相关者沟通?
-AI产品经理需要与利益相关者沟通,让他们理解自己作为科学家的角色以及所进行的创新工作,同时明确AI不是万能的解决方案,而是一种可能需要投入大量时间和努力的工具。
AI产品经理如何确保团队使用正确的数据集?
-AI产品经理需要确保团队使用正确、更新且完整的数据集,并建立警报系统以防止AI使用错误或过时的数据。
AI产品经理应如何处理AI项目的失败风险?
-AI产品经理应该能够评估哪些AI项目值得投资,准备好接受失败,并从中学习,同时保持对新技术和算法的关注,以便不断优化产品。
AI产品经理如何提升自己的专业技能?
-AI产品经理应该持续关注科学进步,学习新的算法和技术,以保持自己的专业知识处于行业前沿。
AI产品经理在推动AI项目时应该注意什么?
-AI产品经理在推动AI项目时应该主动提出最佳的实验和方法,组织培训会议,提高公司对AI的理解,并确保AI的应用是可行且有效的。
AI产品经理如何平衡创新与实际应用?
-AI产品经理需要在推动创新的同时,评估项目的可行性和实际效益,确保投入的资源能够得到合理的回报。
AI产品经理如何确保AI输出的准确性?
-AI产品经理需要确保输入到AI系统的数据是准确和高质量的,同时定期检查和调整AI模型,以保持其输出的准确性。
Outlines
🤖 人工智能产品经理的角色与挑战
本段视频介绍了人工智能产品经理的职责和特点。人工智能产品经理是专业领域的专家,负责引导团队构建最佳的AI服务解决方案。与通用产品经理不同,AI产品经理需要具备深厚的专业知识,通常达到博士水平。他们不仅要掌握不同的算法和技术,还要能够识别哪些项目适合采用AI技术,并对项目的成功有预见性。此外,AI产品经理还需要领导实验团队,处理常规产品路线图之外的事务,并且能够处理AI项目的风险和不确定性。
🚀 如何成为一名优秀的人工智能产品经理
这一部分讨论了如何成为一名成功的AI产品经理。首先,沟通是关键,需要让利益相关者理解AI的创新性和可能的局限性。其次,作为团队的领导者,AI产品经理需要具备微观管理能力,指导团队构建产品、收集数据,并确保数据集的正确性和有效性。此外,AI产品经理需要设定评估标准,组织培训会议,提升公司对AI的理解,并且保持对科学进展的更新,以便在产品中应用新的算法和技术。最终,AI产品经理需要具备辨别科学论文真伪的能力,以确保尝试的AI解决方案是有价值的。
Mindmap
Keywords
💡人工智能产品经理
💡通用产品经理
💡专家
💡机器学习
💡风险
💡创新
💡数据集
💡沟通
💡实验团队
💡进步
💡效率
Highlights
AI产品经理是领域专家,使用待办事项列表指导团队构建最佳的AI服务解决方案。
AI产品经理与API产品经理类似,但需要成为深度学习和机器学习方面的绝对专家。
AI产品经理需要了解不同算法、技术和配置,以解决特定产品的问题。
AI产品经理往往需要承担高风险的项目,但也可能带来巨大的成功。
AI产品经理通常领导一个实验团队,这个团队独立于常规产品路线图。
谷歌翻译从以前的翻译模式转变为使用机器学习作为翻译引擎,准确度显著提高。
AI产品经理可以将现有产品通过AI进行改进,如使用机器学习为零售产品建立推荐系统。
作为AI产品经理,沟通是关键,需要让利益相关者理解AI的创新性和可能的局限性。
AI不是解决所有问题的万能钥匙,它只是一种可能的工具。
AI产品经理需要指导团队构建产品,收集数据,并做出决策。
AI产品经理需要评估团队的进展,并确保使用的数据集正确。
AI产品经理应能够定义指标,评估数据集的效率,并确保数据的正确性。
AI产品经理需要建立警报系统,以避免AI使用不完整或过时的数据。
AI产品经理应主动提出最佳的实验和方法,使用AI改进产品。
AI产品经理应组织培训,让公司了解AI及其进展。
AI产品经理需要保持与科学进展同步,以了解新算法和技术。
AI产品经理要判断科学论文的实际应用价值,决定是否尝试新解决方案。
Transcripts
in today's video i'll tell you
everything you need to know about ai
artificial intelligence product managers
how are they different from generalist
product managers and what you need to
know to excel at this role
let's begin
hi i'm dr bob and i'll be your product
management teacher continuing the series
of telling you about different types of
product managers today we'll be looking
at ai product managers as always let's
start with the definition
ai product manager is a subject level
expert who uses backlog to guide a
dedicated team to build best possible ai
based services solutions this
position this specialist is again pretty
similar to an api product manager
but here
apart from being a merge of product
manager project manager and a tech lead
you have to be an absolute subject
mother expert and that goes to a
doctorate level very often and funny
enough my doctorate comes from the area
of ai which is not even the ai needed
the ai used by the modern
industry
and i wouldn't be an ai product manager
with my doctorate so
it's very hard to find the actual niche
of the deep learning machine learning
experts who can grow into ai product
managers and we either will get experts
doctors for the university growing into
the pm role or ai developers who will
have that
spark of product manager in them in
order to grow into this specific role
that translates into knowledge of
different algorithms different
techniques different configurations
that can be used in any given problem
and will be most likely to succeed in
solving it for the specific product
it's very difficult it will not always
work and there's a lot of risk involved
but you are the expert to well play your
cards and see which efforts
which ai projects are the best one to
take for your company and product thus
you have to also understand which
projects can you take on which of them
will make sense and have
that innovation spark the initiative to
propose how to use those ai
aspects those ai technologies to
better serve your clients and users
the ai product manager will often lead
an experimental
team that's on the side of the regular
roadmap so that the product doesn't have
to rely on the results because they
won't always be good and
they won't always pan out the way it's
planned but if they do they work
brilliantly there was a case few years
back where google translate switched
from their previous mode of translating
to one that uses
machine learning
as the
foundation as the engine for translation
and the results blew the mind the
accuracy i believe of the english
translations went from like 50 to 70 80
90. i'll check again
while i'm editing and put the numbers on
the screen because it's something that
came up to me when i started recording
and i didn't plan to mention it but i
think it's very very relevant which only
shows that one of the projects an ai
product manager can take is to take
something that exists already and see
whether it will perform better using ai
and the data collected thus far for
example in a retail product to
use the machine learning for a
recommendation system to suggest
additional stuff to buy
so
how to excel as an ai product manager
for one thing
communication here will be the key
your stakeholders need to understand
that you are a scientist that you are
doing something innovative and that it
might not work ai is not a golden
universal solution to any problem it's
just a tool
one of many that can be used and
sometimes it will be
successful in the end but it might take
too much time too much effort to achieve
and something simpler might as well work
brilliantly
very early on so
a you shouldn't use that as a holy grail
of development and as mentioned you need
to decide whether something is worth
investing in or not be ready to be that
micromanaging product manager you will
have to guide your team exactly on what
they need to build how they need to
build what data they need to collect
this kind of stuff they will heavily
rely on your expert level knowledge in
the field and you make the calls
obviously you have to also make sure
that you teach them on the way but you
are the head of this project
you will need to evaluate whether the
team is making progress
and you also need to be there to make
sure that the data set that the
artificial intelligence is to be used is
correct
and
choose metrics to use to evaluate them
as well you need to be able to
define metrics
that will
characterize the data set whether it's
efficient or not and whether you always
have the right data for the
artificial intelligence
you need to have alerting system so that
the
recommendations the output from ai
doesn't use all data by accident or it's
using data that's not being updated or
is incomplete because then you will get
mumbo jumbo and if the data the input is
broken even if the ai engine is correct
and it has been proven to work
effectively
it will start putting out
nonsense if nonsense is put inside be
proactive
suggest to the team and stakeholders the
best experiments the best approaches you
can take in the product to use ai
make sure you organize training sessions
for the company so they know more about
the ai more about the team or about the
progress
make it public and make people
understand that ai can work but it can't
it's just a tool and you are the expert
in this particular tool and just like
with other specialist product managers
make sure to spend a little time each
week to evaluate the solution you have
whether it can work better or quicker
but on top of that
be sure to stay up to date with the
progress of science so that you are on
top of any new algorithms any new
techniques that might appear
and you
can determine whether the
solutions that appeared that have been
described by scientists can be applied
in your product can give you better
results
is it worth trying or is a scientific
paper just
paper written to help some scientists
get his or her funding it's up to you to
decide you are the expert that should
know better and there you have it that's
all i have for you for the ai product
manager
and remember a specialist product
manager is someone on top of a regular
product manager and to become a great
product manager
make sure to check out my courses on
udemy skillshare the links are now
on the screen and i'll see you in the
next video
see you then
[Music]
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