Learn about the latest and greatest in machine learning (ML) from Google! We cover what’s available to developers when it comes to creating, understanding, and deploying models for a variety of different applications. From Responsible AI to TensorFlow 2.5, mobile devices, microcontrollers, and beyond. We cover new releases and tools, and you hear about the latest from the Google Cloud Platform, and how to enable an end-to-end machine learning pipeline.
ML Kit: turnkey APIs to use on-device ML in mobile apps → https://goo.gle/3elLJv9
TensorFlow Hub for real world impact → https://goo.gle/2QNjY5H
Machine learning for next gen web apps with TensorFlow.js → https://goo.gle/3v6cwBg
Speakers: Kemal El Moujahid, Sarah Sirajuddin, Craig Wiley
TensorFlow at Google I/O 2021 Playlist → https://goo.gle/io21-TensorFlow-1
All Google I/O 2021 Keynotes → https://goo.gle/io21-keynotes
All Google I/O 2021 Sessions → https://goo.gle/io21-allsessions
Subscribe to TensorFlow → https://goo.gle/TensorFlow
product: TensorFlow – General; event: Google I/O 2021; fullname: Kemal El Moujahid, Sarah Sirajuddin, Craig Wiley; re_ty: Premiere;
PyData LA 2018
I will present some way in which tensor methods can be combined with deep learning, and demonstrate through Jupyter notebooks on how easy it is specify tensorized neural networks.
PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R.
PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.
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In this video, watch this special keynote talk from Hilary Mason about “The Present and Future of Artificial Intelligence and Machine Learning” during the Open Data Science Conference in Boston 2019. Hilary is a data scientist at Accel Partners, as well as the founder of technology at her startup, Fast Forward Labs.
Do You Like This Video? Share Your Thoughts in Comments Below
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Today, thousands of scientists and engineers are applying machine learning to an extraordinarily broad range of domains, and over the last five decades, researchers have created literally thousands of machine learning algorithms. Traditionally an engineer wanting to solve a problem using machine learning must choose one or more of these algorithms to try, and their choice is often constrained by their familiar with an algorithm, or by the availability of software implementations. In this talk we talk about ‘model-based machine learning’, a new approach in which a custom solution is formulated for each new application. We show how probabilistic graphical models, coupled with efficient inference algorithms, provide a flexible foundation for model-based machine learning, and we describe several large-scale commercial applications of this framework. We also introduce the concept of ‘probabilistic programming’ as a powerful approach to model-based machine learning, and we discuss a specific probabilistic programming language called Infer.NET, which has been widely used in practical applications.
See more on this video at https://www.microsoft.com/en-us/research/event/faculty-summit-2017/
Learn Artificial Intelligence from leading experts and attain a Dual Certificate in AI and Machine Learning from world-renowned universities. Take the step towards your professional growth by obtaining expertise in the real-world application of the latest technological tools of AI. Over 500+ Hiring Partners & 8000+ career transitions over varied domains.
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For data sets, code files and projects associated with course please enroll for free at: https://www.greatlearning.in/academy/learn-for-free/courses/machine-learning-with-python
Machine learning is changing the world that we live in. Top companies such as Facebook, Google, Microsoft and Amazon are looking for machine learning engineers and the average salary of a machine learning engineer is around 120k$ dollars.
Visit Great Learning Academy, to get access to 80+ free courses with 1000+ hours of content on Data Science, Data Analytics, Artificial Intelligence, Big Data, Cloud, Management, Cybersecurity and many more. These are supplemented with free projects, assignments, datasets, quizzes. You can earn a certificate of completion at the end of the course for free. https://glacad.me/3uBbddU
Get the free Great Learning App for a seamless experience, enroll for free courses and watch them offline by downloading them. https://glacad.me/3cSKlNl
This full course on Machine Learning with Python will be taught by Dr Abhinanda Sarkar. Dr Sarkar is the Academic Director at Great Learning for Data Science and Machine Learning Programs. He is ranked amongst the Top 3 Most Prominent Analytics & Data Science Academicians in India.
He has taught applied mathematics at the Massachusetts Institute of Technology (MIT) as well as been visiting faculty at Stanford and ISI and continues to teach at the Indian Institute of Management (IIM-Bangalore) and the Indian Institute of Science (IISc).
These are the topics covered in this session:
-Agenda – 0:00
-Introduction to Python and Anaconda – 3:58
-Introduction to Pandas and Data Manipulation – 1:07:05
-Introduction to Numpy and Numerical Computing – 4:42:32
-Data Visualization – 5:10:58
-Statistics vs Machine Learning – 6:06:12
-Types of Statistics – 6:12:44
-Understanding Data – 7:54:39
-What is Reinforcement Learning? – 7:58:19
-Reinforcement Learning Framework – 8:53:46
-Q-Learning – 9:24:58
-Case Study on Smart Taxi – 9:51:08
Read more on Machine Learning
You can check out our other full course videos:
Python for Data Science: https://www.youtube.com/watch?v=edvg4eHi_Mw&t=17638s
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Tableau Training for Beginners: https://www.youtube.com/watch?v=6mBtTNggkUk&t=2s
Time Series Analysis: https://www.youtube.com/watch?v=FPM6it4v8MY&t=8433s
Probability and Statistics: https://www.youtube.com/watch?v=z9siRCCElls&t=4844s
Machine Learning Salary Trends in India : https://www.mygreatlearning.com/blog/machine-learning-salary-in-india/
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An educational video made by Ted to help people to understand the machine intelligence.
A Wharton professor and tech entrepreneur examines how algorithms and artificial intelligence are starting to run every aspect of our lives, and how we can shape the way they impact us
Through the technology embedded in almost every major tech platform and every web-enabled device, algorithms and the artificial intelligence that underlies them make a staggering number of everyday decisions for us, from what products we buy, to where we decide to eat, to how we consume our news, to whom we date, and how we find a job. We’ve even delegated life-and-death decisions to algorithms–decisions once made by doctors, pilots, and judges. In his new book, Kartik Hosanagar surveys the brave new world of algorithmic decision-making and reveals the potentially dangerous biases they can give rise to as they increasingly run our lives. He makes the compelling case that we need to arm ourselves with a better, deeper, more nuanced understanding of the phenomenon of algorithmic thinking. And he gives us a route in, pointing out that algorithms often think a lot like their creators–that is, like you and me.
Hosanagar draws on his experiences designing algorithms professionally–as well as on history, computer science, and psychology–to explore how algorithms work and why they occasionally go rogue, what drives our trust in them, and the many ramifications of algorithmic decision-making. He examines episodes like Microsoft’s chatbot Tay, which was designed to converse on social media like a teenage girl, but instead turned sexist and racist; the fatal accidents of self-driving cars; and even our own common, and often frustrating, experiences on services like Netflix and Amazon. A Human’s Guide to Machine Intelligence is an entertaining and provocative look at one of the most important developments of our time and a practical user’s guide to this first wave of practical artificial intelligence.
Description by Amazon.com
Futurists have been predicting the rise of self-aware artificial intelligence for decades, prompting Thomas Hornigold to quip that AI has been just 20 years away since 1956.
Read more: https://www.richardvanhooijdonk.com/en/blog/ai-and-morals-should-we-can-we-teach-human-morality-to-machine-minds
In this video, GovBrain Founder and CEO Brent M. Eastwood discusses the ethics of artificial intelligence and machine learning.
Dr. Eastwood explains how to ensure that the machine can ultimately be controlled by human beings. The importance of having ethical, moral, and virtuous human beings train the machine is paramount in this construct. Dr. Eastwood also talks about how the current status of artificial intelligence, machine learning and data science can be used to conduct a Turing Test to determine that perhaps the state of the art is not as advanced as we think. Dr. Eastwood then takes a longer look at robotics and the ethics of singularity.
This video was part of the “With the Best Artificial Intelligence Conference” in September of 2016.
Denise Howell, J. Michael Keyes and Amanda Levendowski discuss the Moral Machine, an MIT Media Lab platform “for gathering a human perspective on moral decisions made by machine intelligence.”
For the full episode, visit https://twit.tv/twil/356
On Monday, April 15, NYU Stern’s Fubon Center for Technology, Business and Innovation hosted a talk on “AI in Business: Machine Learning, Ethics, and Fairness” by Dr. Solon Barocas.
The Komatsu D61EXi/PXi-23 Intelligent Machine Control dozer offers an outstanding improvement in productivity with its innovative and fully automatic blade control function that performs both rough dozing and fi nish grade in automatic mode. All machine control components are integrated into the dozer at the factory, and work together with other Komatsu machine parts to deliver optimal production levels. With a super-slant nose and rear mounted cooler, the D61EXi/PXi-23 dozer is reliable and versatile and offers the best value for your money.
In fact, it is unique on the market today.
Can an algorithm help you improve your penalty kick or tennis serve? In this episode of Making with Machine Learning, Dale Markowitz chats with Machine Learning Engineer Zack Akil to learn about how Google Cloud’s ML services, like Cloud AutoML vision and the Video Intelligence API, can be used to analyze, assess, and improve your game.
0:00 – Introduction
0:40 – Overview
2:05 – What was measured?
3:37 – What powered the demo?
4:12 – Problems/Challenges
4:45 – Training Auto ML Vision model
5:50 – Using it for tennis serve
Blog Post → https://goo.gle/3etkKdM
Code → https://goo.gle/3h4hIOZ
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Product: Video Intelligence API, Cloud AutoML Vision; fullname: Dale Markowitz;
In this episode I’m joined by Jeff Dean, Google Senior Fellow and head of the company’s deep learning research team Google Brain, who I had a chance to sit down with last week at the Googleplex in Mountain View.
As you’ll hear, I was very excited for this interview, because so many of Jeff’s contributions since he started at Google in ‘99 have touched my life and work. In our conversation, Jeff and I dig into a bunch of the core machine learning innovations we’ve seen from Google. Of course we discuss TensorFlow, and its origins and evolution at Google. We also explore AI acceleration hardware, including TPU v1, v2 and future directions from Google and the broader market in this area. We talk through the machine learning toolchain, including some things that Googlers might take for granted, and where the recently announced Cloud AutoML fits in. We also discuss Google’s process for mapping problems across a variety of domains to deep learning, and much, much more. This was definitely one of my favorite conversations, and I’m pumped to be able to share it with you.
The notes for this show can be found at twimlai.com/talk/124.
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Prof. Edmond Awad
(Institute for Data Science and Artificial Intelligence at the University of Exeter)
I describe the Moral Machine, an internet-based serious game exploring the many-dimensional ethical dilemmas faced by autonomous vehicles. The game enabled us to gather 40 million
decisions from 3 million people in 200 countries/territories. I report the various preferences estimated from this data, and document interpersonal differences in the strength of these preferences. I also report cross-cultural ethical variation and uncover major clusters of countries exhibiting substantial differences along key moral preferences. These differences correlate with modern institutions, but also with deep cultural traits. I discuss how these three layers of
preferences can help progress toward global, harmonious, and socially acceptable principles for machine ethics. Finally, I describe other follow up work that build on this project.
Bio: Edmond Awad is a Lecturer (Assistant Professor) in the Department of Economics and the Institute for Data Science and Artificial Intelligence at the University of Exeter. He is also an
Associate Research Scientist at the Max Planck Institute for Human Development, and is a Founding Editorial Board member of the AI and Ethics Journal, published by Springer. Before joining the University of Exeter, Edmond was a Postdoctoral Associate at MIT Media Lab (2017-2019). In 2016, Edmond led the design and development of Moral Machine, a website that gathers human decisions on moral dilemmas faced by driverless cars. The website has been visited by over 4 million users, who contributed their judgements on 70 million dilemmas. Another website that
Edmond co-created, called MyGoodness, collected judgements over 2 million charity dilemmas.
Edmond’s work appeared in major academic journals, including Nature, PNAS, and Nature Human Behaviour, and it has been covered in major media outlets including The Associated Press, The
New York Times, The Washington Post, Der Spiegel, Le Monde and El Pais. Edmond has a bachelor degree (2007) in Informatics Engineering from Tishreen University (Syria), a master’s degree (2011) in Computing and Information Science and a PhD (2015) in Argumentation and Multi-agent systems from Masdar Institute (now Khalifa University; UAE), and a master’s degree (2017) in Media Arts and Sciences from MIT. Edmond’s research interests are in the areas of AI, Ethics, Computational Social Science and Multi-agent Systems.
Machine Learning Python Weather Prediction
In this video I give machine learning with python a go. And I build a machine learning model for predicting the weather in the future.
If you want to learn machine learning I do attempt to explain how machine learning works at 7:02 in the video. As kind of an intro to machine learning.
Articles ranked from most useful to useful but less useful:
Github repo: https://github.com/KalleHallden/WeatherPredictor
“Clean Code Friday”
If you want to receive one short email from me every week, where I go through a few of the most useful things I have explored and discovered this week. Things like; favourite apps, articles, podcasts, books, coding tips and tricks. Then feel free to join https://kalletech.com/clean-code-friday/
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🔥Edureka and NIT Warangal Post Graduate Program on AI and Machine Learning: https://www.edureka.co/post-graduate/machine-learning-and-ai
This Edureka Session explores and analyses the spread and impact of the novel coronavirus pandemic which has taken the world by storm with its rapid growth. In this session, we shall develop a machine learning model in Python to analyze what has been its impact so far and analyze the outbreak of COVID 19 across various regions, visualize them using charts and tables, and predict the number of upcoming confirmed cases.
Finally, we’ll conclude with a few safety measures that you can take to save yourself and your loved ones from getting adversely affected in the hour of crisis.
02: 53 Introduction to COVID 19
05:49 Case Study: the outbreak of COVID 19
🔸Datasets and code: https://bit.ly/3tFxZQa
🔸Machine Learning Tutorial Playlist: https://goo.gl/UxjTxm
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(450+ Hrs || 9 Months || 20+ Projects & 100+ Case studies)
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How it Works?
1. This is a 5 Week Instructor led Online Course,40 hours of assignment and 20 hours of project work
2. We have a 24×7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course.
3. At the end of the training you will be working on a real time project for which we will provide you a Grade and a Verifiable Certificate!
About the Course
Edureka’s Python Online Certification Training will make you an expert in Python programming. It will also help you learn Python the Big data way with integration of Machine learning, Pig, Hive and Web Scraping through beautiful soup. During our Python Certification training, our instructors will help you:
1. Master the Basic and Advanced Concepts of Python
2. Understand Python Scripts on UNIX/Windows, Python Editors and IDEs
3. Master the Concepts of Sequences and File operations
4. Learn how to use and create functions, sorting different elements, Lambda function, error handling techniques and Regular expressions ans using modules in Python
5. Gain expertise in machine learning using Python and build a Real Life Machine Learning application
6. Understand the supervised and unsupervised learning and concepts of Scikit-Learn
7. Master the concepts of MapReduce in Hadoop
8. Learn to write Complex MapReduce programs
9. Understand what is PIG and HIVE, Streaming feature in Hadoop, MapReduce job running with Python
10. Implementing a PIG UDF in Python, Writing a HIVE UDF in Python, Pydoop and/Or MRjob Basics
11. Master the concepts of Web scraping in Python
12. Work on a Real Life Project on Big Data Analytics using Python and gain Hands on Project Experience
Why learn Python?
Programmers love Python because of how fast and easy it is to use. Python cuts development time in half with its simple to read syntax and easy compilation feature. Debugging your programs is a breeze in Python with its built in debugger.
Python has evolved as the most preferred Language for Data Analytics and the increasing search trends on python also indicates that Python is the next “Big Thing” and a must for Professionals in the Data Analytics domain.
For more information, please write back to us at firstname.lastname@example.org or call us at IND: 9606058406 / US: 18338555775 (toll-free).
Artificial intelligence might be a technological revolution unlike any other, transforming our homes, our work, our lives; but for many – the poor, minority groups, the people deemed to be expendable – their picture remains the same.
“The way these technologies are being developed is not empowering people, it’s empowering corporations,” says Zeynep Tufekci, from the University of North Carolina.. “They are in the hands of the people who hold the data. And that data is being fed into algorithms that we don’t really get to see or understand that are opaque even to the people who wrote the programme. And they’re being used against us, rather than for us.”
In episode two of The Big Picture: The World According to AI we examine practices such as predictive policing, predictive sentencing, as well as the power structures and in-built prejudices that could lead to even more harm than the good its champions would suggest.
In the United States, we travel to one of the country’s poorest neighbourhoods, Skid Row in Los Angeles, to see first-hand how the Los Angeles Police Department is using algorithmic software to police a majority black community.
And in China, we examine the implications of a social credit scoring system that deploys machine learning technologies – new innovations in surveillance and social control that are claimed to be used against ethnic Uighur communities.
As AI is used to make more and more decisions for and about us, from targeting, to policing, to social welfare, it raises huge questions. What will AI be used for in the future? And who will stand to benefit?
Watch Episode 1 here: https://youtu.be/134huBl7MAA
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Lecture 10 introduces translation, machine translation, and neural machine translation. Google’s new NMT is highlighted followed by sequence models with attention as well as sequence model decoders.
Natural Language Processing with Deep Learning
– Chris Manning
– Richard Socher
Natural language processing (NLP) deals with the key artificial intelligence technology of understanding complex human language communication. This lecture series provides a thorough introduction to the cutting-edge research in deep learning applied to NLP, an approach that has recently obtained very high performance across many different NLP tasks including question answering and machine translation. It emphasizes how to implement, train, debug, visualize, and design neural network models, covering the main technologies of word vectors, feed-forward models, recurrent neural networks, recursive neural networks, convolutional neural networks, and recent models involving a memory component.
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A day in the life of what it’s like to be a machine learning engineer.
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My AI Masters Degree – https://bit.ly/AIMastersDegree
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Montezuma Article – https://medium.com/@awjuliani/on-solving-montezumas-revenge-2146d83f0bc3
Pommerman NIPS competition – https://www.pommerman.com
Gym Playground – http://gym.openai.com/docs/
OpenAI PPO Baseline Algorithm – https://blog.openai.com/openai-baselines-ppo/
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AI With The Best hosted 50+ speakers and hundreds of attendees from all over the world on a single platform on October 14-15, 2017. The platform held live talks, Insights/Questions pages, and bookings for 1-on-1s with speakers.
We will discuss multiple ways in which healthcare data is acquired and machine learning methods are currently being introduced into clinical settings. This will include: 1) Modeling disease trends and other predictions, including joint predictions of multiple conditions, from electronic health record (EHR) data using Gaussian processes. 2) Predicting surgical complications and transfer learning methods for combining databases 3) Using mobile apps and integrated sensors for improving the granularity of recorded health data for chronic conditions and 4) The combination of mobile app and social network information in order to predict the spread of contagious disease. Current work in these areas will be presented and the future of machine learning contributions to the field will be discussed.
Katherine Heller, Duke University
Computational Challenges in Machine Learning
Melanie Cook, Tedx Speaker, Managing Director Singapore Campus, Hyper Island
| Augmented Intelligence: Where Man and Machine Make Magic | Experience Stage | September 13, 2018 | 12:55 – 13:15
Categories: Future | AI
The Super Intelligentsia believes that AI and Armageddon go hand in hand. Robotics and AI have integrated human and mechanical capabilities at work, with jobs lost and skills condensed to a keystroke. But human intelligence is far from obsolete. With crowd-computing we have knowledge exchanges like Wiki, and real-time curated news. Semantic technology helps leaders to understand what is happening in the work place. But neurology shows that these leaders cannot make choices, and therefore take action, without emotion. Augmented Intelligence takes human intuition and imagination, and combines it with AI’s ability to automate and scale, making the Intelligent Workplace hard to beat.
More about Future:
There are a lot of things you can do to learn Machine Learning. There are resources like books and courses you can follow, competitions you can enter and tools you can use.
And this course is one of the best awesome to begin with. It tought by an professor at Standford University.
Welcome to AI, to Machine Learning.
if you would like to support my work:
Let’s check out what are the 5 must-have skills to become a machine learning engineer.
First, let’s understand what machine learning is.
In simple words.,
Machine learning is all about making the computers to perform intelligent tasks without explicitly coding. This is achieved by training the computer with lots and lots of data.
For example: Detecting whether a mail is a spam or not, recognizing handwritten
digits, Fraud detection in Transactions… and many such applications…
Now let’s see what are the top 5 skills to get a machine learning job.
1). At number 1, we have
Math Skills: Under math skills, we need to know probability and statistics, linear algebra
Probability and Statistics: Machine learning is very much closely related to statistics.
You need to know the fundamentals of statistics and probability theory,
descriptive statistics, Baye’s rule and random variables,
sampling, hypothesis testing, regression and decision analysis.
Linear Algebra: You need to know how to with matrices and some basic operations on matrices such as matrix addition,
subtraction, scalar and vector multiplication,
inverse, transpose and vector spaces.
Calculus: In calculus, you need to know the basics of differential and integral calculus.
2). At number two we have
Programming skills: A little bit of coding skills is enough. But it’s preferred to have the knowledge of data structures, algorithms and Object Oriented Programming (or OOPs) concepts.
Some of the popular programming languages to learn for machine learning is Python, R, Java, and C++.
It’s your preference to master any one programming language. But its advisable
to have a little understanding of other languages and what their advantages and disadvantages are over your preferred one.
3). At number 3 we have
Data engineer skills: Ability to work with large amounts of data (or big data), Data preprocessing,
the knowledge of SQL and NoSQL, ETL (or Extract Transform and Load) operations,
data analysis and visualization skills.
4). Next, we have
Knowledge of Machine Learning Algorithms: you should be familiar with popular machine learning
algorithms such as linear regression, logistic
regression, decision trees, random forest, clustering (like K means, hierarchical), reinforcement learning and neural networks.
5). And Finally,
The Knowledge of Machine Learning Frameworks:
You Should be Familiar with popular machine learning frameworks such as sci-kit learn, tensorflow, Azure, caffe, theano, spark and torch.
Namaskaar Dosto, is video mein maine aapse Artificial Intelligence aur Machine Learning ke baare mein baat ki hai, Artificial Intelligence aur Machine Learning mein kya difference hai aur kaise yeh hamare kaam aati hai, yahi maine is video mein bataya hai. Mujhe umeed hai ki aapko Machine Learning aur AI ki yeh video pasand aayegi.
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Watch this video to understand Machine Learning Deployment in House Price Prediction.
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Stock Predictions Using Machine Learning Algorithms
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Can we predict the outcome of a football game given a dataset of past games? That’s the question that we’ll answer in this episode by using the scikit-learn machine learning library as our predictive tool.
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This is a sample of Paul R. Daugherty’s audiobook “Human + Machine: Reimagining Work in the Age of AI“. FULL AUDIOBOOK available here: https://audiobooksway.com/full-length?id=B07BDPZN5L
ALTERNATIVE LINK : https://audiobookslist.com/full-length?id=B07BDPZN5L
AI is radically transforming business. Are you ready? Look around you. Artificial intelligence is no longer just a futuristic notion. It’s here right now – in software that senses what we need, supply chains that think in real time, and robots that respond to changes in their environment. Twenty-first-century pioneer companies are already using AI to innovate and grow fast. The bottom line is thi…
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Listen to “Human + Machine: Reimagining Work in the Age of AI” audiobook by Paul R. Daugherty.
Jordan Etem: Driving Innovation