🔥Intellipaat RPA course: https://intellipaat.com/rpa-training/
In this RPA tutorial for beginners video you will learn what is Robotic Process Automation (RPA), and the various tools which can be used to implement the RPA technology. RPA training is in much demand these days so we have come up with this video where we will show you how to create RPA programs using the UiPath Tool. So this RPA UiPath tutorials video is your one stop video for the basic rpa concepts required to get started with this technology.
#RPATutorialForBeginners #UiPathTutorials #RPATraining (More)

Robotic Process Automation – RPA Tutorial for Beginners on Blue Prism (More)

** RPA Training using UiPath – https://www.edureka.co/robotic-process-automation-training **
** RPA Training using Automation Anywhere – https://www.edureka.co/automation-anywhere-certification-training **
This Edureka tutorial video on Blue Prism vs UiPath vs Automation Anywhere will help you in demystifying the fundamental differences between each of these RPA Tools. Following are the topics which are used for comparison:
3:42 Offers Trial Version
4:48 Market Trend
5:18 Based Technologies
5:52 Architecture
6:47 Process Designer
7:30 Programming Skills
8:36 Accessibility
9:01 Re-usability
9:40 Recorders
10:20 Robots
11:16 Accuracy
12:16 Operational Scalability
12:43 Community & Support
13:06 Jobs Related To Tools
13:40 Certification (More)

The path to skill around the globe has been the same for thousands of years: train under an expert and take on small, easy tasks before progressing to riskier, harder ones. But right now, we’re handling AI in a way that blocks that path — and sacrificing learning in our quest for productivity, says organizational ethnographer Matt Beane. What can be done? Beane shares a vision that flips the current story into one of distributed, machine-enhanced mentorship that takes full advantage of AI’s amazing capabilities while enhancing our skills at the same time. (More)

These robots milk cows when they demand it.
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http://en.triowin.com/citrus-processingline-15266140703740335.html
Shanghai Triowin Intelligent Machinery Co., Ltd import the most advanced technology from Italy, Europe and America, form brilliant technical program for citrus processing line. Customized design is available based on the investment and actual production situations of enterprises, realize real turn-key project for customer. (More)

Mahindra Bolero ZLX Voice Messaging System (More)

This video covers Stanford CoreNLP Example. (More)

Transfer Learning in Natural Language Processing (NLP): Open questions, current trends, limits, and future directions. Slides: https://tinyurl.com/FutureOfNLP
A walk through interesting papers and research directions in late 2019/early-2020 on:
– model size and computational efficiency,
– out-of-domain generalization and model evaluation,
– fine-tuning and sample efficiency,
– common sense and inductive biases.
by Thomas Wolf (Science lead at HuggingFace) (More)

Hi, everyone. You are very welcome to week two of our NLP course. And this week is about very core NLP tasks. So we are going to speak about language models first, and then about some models that work with sequences of words, for example, part-of-speech tagging or named-entity recognition. All those tasks are building blocks for NLP applications. And they’re very, very useful. So first thing’s first. Let’s start with language models. Imagine you see some beginning of a sentence, like This is the. How would you continue it? Probably, as a human,you know that This is how sounds nice, or This is did sounds not nice. You have some intuition. So how do you know this? Well, you have written books. You have seen some texts. So that’s obvious for you. Can I build similar intuition for computers? Well, we can try. So we can try to estimate probabilities of the next words, given the previous words. But to do this, first of all,we need some data. So let us get some toy corpus. This is a nice toy corpus about the house that Jack built. And let us try to use it to estimate the probability of house, given This is the. So there are four interesting fragments here. And only one of them is exactly what we need. This is the house. So it means that the probability will be one 1 of 4. By c here, I denote the count. So this the count of This is the house,or any other pieces of text. And these pieces of text are n-grams. n-gram is a sequence of n words. So we can speak about 4-grams here. We can also speak about unigrams, bigrams, trigrams, etc. And we can try to choose the best n,and we will speak about it later. But for now, what about bigrams? Can you imagine what happens for bigrams, for example, how to estimate probability of Jack,given built? Okay, so we can count all different bigrams here, like that Jack, that lay, etc., and say that only four of them are that Jack. It means that the probability should be 4 divided by 10. So what’s next? We can count some probabilities. We can estimate them from data. Well, why do we need this? How can we use this? Actually, we need this everywhere. So to begin with,let’s discuss this Smart Reply technology. This is a technology by Google. You can get some email, and it tries to suggest some automatic reply. So for example, it can suggest that you should say thank you. How does this happen? Well, this is some text generation, right? This is some language model. And we will speak about this later,in many, many details, during week four. So also, there are some other applications, like machine translation or speech recognition. In all of these applications, you try to generate some text from some other data. It means that you want to evaluate probabilities of text, probabilities of long sequences. Like here, can we evaluate the probability of This is the house, or the probability of a long,long sequence of 100 words? Well, it can be complicated because maybe the whole sequence never occurs in the data. So we can count something, but we need somehow to deal with small pieces of this sequence, right? So let’s do some math to understand how to deal with small pieces of this sequence. So here, this is our sequence of keywords. And we would like to estimate this probability. And we can apply chain rule,which means that we take the probability of the first word, and then condition the next word on this word, and so on. So that’s already better. But what about this last term here? It’s still kind of complicated because the prefix, the condition, there is too long. So can we get rid of it? Yes, we can. So actually, Markov assumption says you shouldn’t care about all the history. You should just forget it. You should just take the last n terms and condition on them, or to be correct, last n-1 terms. So this is where they introduce assumption, because not everything in the text is connected. And this is definitely very helpful for us because now we have some chance to estimate these probabilities. So here, what happens for n = 2, for bigram model? You can recognize that we already know how to estimate all those small probabilities in the right-hand side,which means we can solve our task. So for a toy corpus again,we can estimate the probabilities. And that’s what we get. Is it clear for now? I hope it is. But I want you to think about if everything is nice here. Are we done? (More)

Check out the official A.I.: Artificial Intelligence (2001) trailer starring Haley Joel Osment! Let us know what you think in the comments below.
► Buy or Rent on FandangoNOW: https://www.fandangonow.com/details/movie/ai-artificial-intelligence-2001/MMVE115A66D03AADABDDA48C54B443BEB535?ele=searchresult&elc=a.i.%20&eli=0&eci=movies?cmp=MCYT_YouTube_Desc (More)

The tech billionaire tweets about the famous cognitive scientist’s comprehension of artificial intelligence.
» Subscribe to CNBC: http://cnb.cx/SubscribeCNBC (More)

SASTRA Day 4, Session 01 ATAL AICTE FDP on AI,ML u0026DL 2020 09 16 at 20 42 GMT 7
Workshop topics
– Introduction to Artificial Intelligence
– Introduction to Python
– Introduction to Internet of Things(IoT)
– Problem Formulations & Representations
– Uninformed and Informed Search Algorithms
– Knowledge Representation and different types of Knowledge Representation
– Ontology Engineering
– Fuzzy and Temporal Logic Systems
– Natural Language Processing
– Machine Learning and Deep Learning
– Reinforcement Learning
– Application and current trends of AI
– Sample Problems
– Case Studies & hands-on Coding using Python for the above topics
Full playlist: https://www.youtube.com/playlist?list=PL7Ld-_6sF_dqunfOyV4TuztyvbbhxZWJO (More)

SASTRA Day 4, Session 04 ATAL AICTE FDP on AI,ML u0026DL 2020 09 17 at 02 45 GMT 7
Workshop topics
– Introduction to Artificial Intelligence
– Introduction to Python
– Introduction to Internet of Things(IoT)
– Problem Formulations & Representations
– Uninformed and Informed Search Algorithms
– Knowledge Representation and different types of Knowledge Representation
– Ontology Engineering
– Fuzzy and Temporal Logic Systems
– Natural Language Processing
– Machine Learning and Deep Learning
– Reinforcement Learning
– Application and current trends of AI
– Sample Problems
– Case Studies & hands-on Coding using Python for the above topics
Full playlist: https://www.youtube.com/playlist?list=PL7Ld-_6sF_dqunfOyV4TuztyvbbhxZWJO (More)

SASTRA Day 5, Session 01 ATAL AICTE FDP on AI,ML u0026DL 2020 09 17 at 20 35 GMT 7
Workshop topics
– Introduction to Artificial Intelligence
– Introduction to Python
– Introduction to Internet of Things(IoT)
– Problem Formulations & Representations
– Uninformed and Informed Search Algorithms
– Knowledge Representation and different types of Knowledge Representation
– Ontology Engineering
– Fuzzy and Temporal Logic Systems
– Natural Language Processing
– Machine Learning and Deep Learning
– Reinforcement Learning
– Application and current trends of AI
– Sample Problems
– Case Studies & hands-on Coding using Python for the above topics
Full playlist: https://www.youtube.com/playlist?list=PL7Ld-_6sF_dqunfOyV4TuztyvbbhxZWJO (More)

This is to share knowledge on Data science & AI (More)

We have completed this four weeks journey of building the foundations of Artificial Intelligence with passionate learners. (More)

Scary future predictions Elon Musk and Bill Gates. (More)

Artificial Intelligence (AI) is currently the hottest buzzword in tech. We have put together the best clips on Artificial Intelligence by the most well known leaders and influencers such as Bill Gates, Tim Cook, Elon Musk, Sundar Pichai, and Jeff Bezos. AI is predicted to increase economic growth by an average of 1.7 percent across 16 industries by 2035. The report goes on to say that, by 2035, AI technologies could increase labor productivity by 40 percent or more, thereby doubling economic growth in 12 developed nations that continue to draw talented and experienced professionals to work in this domain. Let us see what our business leaders have to say about this. (More)

In today’s video learn the best lessons of billionaires from Elon Musk, Bill Gates, Mark Cuban and more! You’ll get expert advice on how to experiment, be persuasive, love what you do, get some haters, be disruptive, keep experimenting and play it smart. (More)

Today Bill Gates gave an interview to Bloomberg and among other things talked about Tesla CEO Elon Musk saying he built great electric cars with quality and compared him to Steve Jobs per reporter’s request. (More)

Check out these playlists to find out more about Roborace:
http://bit.ly/V_YTSeasonAlpha
http://bit.ly/V_YTGenerationAI
http://bit.ly/V_RoboraceStories (More)

Get your free trial and buy Luminar 4 here: https://macphun.evyy.net/c/2069361/645022/3255
Use the coupon code FSTOPPERS to get extra $10 off (More)

Could artificial intelligence ever gain true consciousness? This documentary explores what might unfold if super intelligent AI acquired consciousness, how it might see itself, and what it’s impact might be on our world and beyond. (More)

Elon Musk has expressed worry about the advent of a digital superintelligent AI numerous times now. He has put solid solutions to the AI control problem. One of which is the merging scenario with AI. But first Elon Musk, is focused on making sure humanity makes the transition to renewable energy, which is the first right step towards becoming a type 1 civilization. (More)

[GLOBAL LEADERS FORUM 2019] 마틴 포드 ( Martin Ford ) AI마인드 저자 -세션3 (More)

CGTN’s Mike Walter spoke with author and writer Martin Ford about the future and impact of robotics in the workplace and in day-to-day life. (More)

big guy and rusty the boy robot (More)

big guy and rusty the boy robot (More)

big guy and rusty the boy robot (More)

What does AI, machine learning and robotics mean to Leaders and Companies? It is the new frontier for business. But how will leaders react, what are their roles and responsibilities. Equally important how will employees and customers react? (More)

What are the leadership challenges that Artificial Intelligence pose in the business world today? How can business leaders be prepared to thrive in this inevitable global trend? (More)

Gyrus is a globally recognized award-winning LMS platform used by millions of professionals across more than 25 countries. A truly futuristic LMS, powered by Artificial Intelligence and Machine Learning. (More)

Gene Roche, executive professor of higher education, discusses the evolution of computer intelligence and the future of human work. (More)

Can robots be racist? It appears that way, per results from Beauty.AI, a beauty competition designed to take prejudices out of the mix by having algorithms do the judging instead of humans.
But results from the competition indicate that even ‘bots have biases, the Guardian reports. Forty-four winners were chosen out of about 6,000 entrants from all over the globe who uploaded pics to Youth Laboratories’ site, allowing the “robot jury” to make its assessments based on supposedly objective criteria such as facial symmetry and how many wrinkles and pimples a person had, TNW.com notes. (More)

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