How Will Generative AI Be Used in the Future? Answer: AutoGen

21

October

2023

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The generative AI we know of today is ChatGPT, Midjourney, and DALL·E 3 and many more. This generative AI is very good and advanced, but there are some flaws, like not being able to perform long iterations. Now there is something new called AutoGen. AutoGen is an open-source project from Microsoft that was released on September 19, 2023. AutoGen at its core, is a generative AI model that works with agents; those agents work together in loops. Agents are in essence, pre-specified workers that can become anything, so there are agents that can code well and agents that can review the generated code and give feedback. Agents can be made to do anything and become experts in any field, from marketing to healthcare.

An example of what AutoGen can do is the following: if I want to write some code to get the stock price of Tesla, I could use ChatGPT, and it will output some code. Most of the time, the code that is written by chatGPT via the OpenAI website will have some errors. But with AutoGen, there are two or more agents at work: one that will output code and the second one that is able to run the code and tell the first model if something is wrong. This process of generating the code and running the code will go on until the code works and results in the correct output. This way, the user does not have to manually run the code and ask to fix the errors or other problems with AutoGen it is done automatically.

I also tried to create some code with AutoGen. I first installed all the necessary packages and got myself an API key for openAI GPT4. Then I started working on the code and decided to create the game “Snake”. Snake is an old and easy game to create, but it might be a challenge for AutoGen. I started the process of creating the snake game, and it had its first good run. I was able to create the first easy version of the game. I then came up with some iterations to improve the game. The game now also has some obstacles that, if the snake bumps into one, the game will end. This was also made by AutoGen without any problems. After palying around, I was really amazed at how powerful this AutoGen is, and I can only imagine what else can be created with AutoGen.

AutoGen is a very promising development and will be the future of professional code development or atomization tasks. If the large language models (LLMs) get more powerful, this AutoGen will also be more powerful because all the individual agents will be more powerful. It is interesting to follow this development and see if this AutoGen could create games that are not yet existing.

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Deepfake Fraud – The Other Side of Artificial Intelligence

8

October

2021

Dangers of AI: How deepfakes through Artificial Intelligence could be used for fraud, scams and cybercrime.

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Together with Machine Learning, Artificial Intelligence (or: AI) can be considered one of if not the hottest emerging innovations in the field of technology nowadays (Duggal, 2021). AI entails the ability of a computer or a machine to ‘think by itself’, as it strives to mimic human intelligence instead of simply executing actions it was programmed to carry out. By using algorithms and historical data, AI utilizes Machine Learning in order to comprehend patterns and how to respond to certain actions, thus creating ‘a mind of its own’ (Andersen, n.d.). 

History

Even though the initial days of Artificial Intelligence research date back to the late 1950s, the technology has just recently been introduced to the general mass on a wider scale. The science behind the technology is complex, however AI is becoming more widely known and used on a day-to-day basis. This is due to the fact that computers have become much faster and data (for the AI to derive from) has become more accessible (Kaplan & Haenlein, 2020). This allows for AI to be more effective, to the point where it has already been implemented in every-day devices i.e. our smartphones. Do you use speech or facial recognition for unlocking your phone? Do you use Siri, Alexa or Google Assistant? Ever felt like advertisements on social media resonate a bit too much with your actual interests? Whether you believe it or not, it is highly likely that both you and I come into contact with AI on a daily basis.

AI in a nutshell: How it connects to Machine/Deep Learning

That’s good… right?

Although the possibilities for positively exploiting AI seem endless, one of the more recent events which shocked the world about the dangers of AI is a phenomenon called ‘deepfaking’. This is where AI utilizes a Deep Learning algorithm to replace a person from a photo/video with someone else, creating seemingly (!) authentic and real visuals of that person. As one can imagine, this results in situations where people seem to be doing things through media, which in reality they have not. Although people fear the usage of this deepfake technology against celebrities or high-status individuals, this can – and actually does – happen to regular people, possibly you and I.

Cybercrime

Just last month, scammers from all over the world are reported to have been creatively using this cybercrime ‘technique’ in order to commit fraud against, scam or blackmail ordinary people (Pashaeva, 2021). From posing as a wealthy bank owner to extract money from investors, to blackmailing people with videos of them seemingly engaging in a sexual act… as mentioned before, the possibilities for exploiting AI seem endless. Deepfakes are just another perfect illustration of this fact. I simply hope that, in time, the positives of AI outweigh the negatives. I would love to hear your perspective on this matter.

Discussion: Deepfake singularity

For example, would you believe this was actually Morgan Freeman if you did not know about Artificial Intelligence and deepfakes? What do you think this technology could cause in the long term, when the AI develops itself into a much more believable state? Will we be able to always spot the fakes? What do you think this could lead to in terms of possible scamming or blackmailing, if e.g. Morgan Freeman were to say other things…?

References

Duggal, N. (2021). Top 9 New Technology Trends for 2021. Available at: https://www.simplilearn.com/top-technology-trends-and-jobs-article

Andersen, I. (n.d.). What Is AI and How Does It Work? Available at: https://www.revlocal.com/resources/library/blog/what-is-ai-and-how-does-it-work

Kaplan, A., & Haenlein, M. (2020). Rulers of the world, unite! The challenges and opportunities of artificial intelligence. Business Horizons, 63(1). https://doi.org/10.1016/j.bushor.2019.09.003

Pashaeva, Y. (2021). Scammers Are Using Deepfake Videos Now. Available at: https://slate.com/technology/2021/09/deepfake-video-scams.html

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Author: Roël van der Valk

MSc Business Information Management student at RSM Erasmus University - Student number: 483426 TA BM01BIM Information Strategy 2022

Hey Podcast Lover! Have You Heard Of Lex Fridman?

7

October

2020

As BIM-student, it is very likely that you are interested in topics like coding, Deep Learning, Artificial Intelligence, Machine Learning, human-robotic interaction, or Autonomous Vehicles. If by any chance you also enjoy listening to podcasts, you might be in luck:

I highly suggest you to check out the Lex Fridman Podcast.

LexFridman

Lex Fridman is an AI research scientist at the Massachusetts Institute of Technology, often better known as MIT. He works on developing deep learning approaches to human sensing, scene understanding, and human-AI interaction. He is particularly interested in applying these technologies in the field of Autonomous Driving.

LexFridmanTeaching

If you know the Joe Rogan Experience, you likely are already familiar with Lex. Having worked for both Google and Tesla, Lex Fridman understands the business application of digital technologies. He uses his podcast to share this knowledge with his audience and discusses his fascination with a variety of interesting guests. This can be particularly interesting for us as Business Information Management students, as we also form the future bridge between business ventures and technological innovation. The podcast discusses similar topics like we get taught in class, sometimes going more in depth, with international research experts in those particular fields.

If you enjoy podcasts, these are some examples of Lex Fridman Podcast episodes that I highly recommend you to give a listen as a BIM-student:
RecommendedEpisodes

  • Episode #31 with George Hotz: Comma.ai, OpenPilot, Autonomous Vehicles.
    Famous security hacker. First to hack the iPhone. First to hack the PlayStation 3. Started Comma.ai to create his own vehicle automation machine learning application. Wants to offer a $1000 automotive driving application, which drivers can use on their phone.

 

  • Episode #49 with Elon Musk: Neuralink, AI, Autopilot, and the Pale Blue Dot.
    Elon Musk. Tech entrepreneur and founder of companies like Tesla, SpaceX, PayPal, Neuralink, OpenAI, and The Boring Company.

 

  • Episode #114 with Russ Tedrake: Underactuated Robotics.
    Professor of Electrical Engineering and Computer Science, Aeronautics and Astronautics, and Mechanical Engineering at MIT.

 

  • Episode #120 with François Chollet: Measures of Intelligence.
    French Software Engineer and researcher in Artificial Intelligence, who works for Google. Author of Keras – keras.io – a leading deep learning framework for Python, used by organisations such as CERN, Microsoft Research, NASA, Netflix, Yelp, Uber, and Google.

These were just several examples of episodes that I enjoyed myself.

The benefit of a podcast is that you can listen it basically anywhere, and can stop listening at any time. If you are not familiar with podcasts yet or with the listening experience they offer, maybe the Lex Fridman Podcast could be your first step into this experience.

You can find the episodes of the Lex Fridman Podcast here: https://lexfridman.com/podcast/

Or check out Lex Fridman’s Youtube channel here: https://www.youtube.com/user/lexfridman

The above sources have been used as sources for this post. 5/5 (7)

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Could AI prevent suicide?

9

October

2019

3.5/5 (2) Is your phone spying on you? This question runs through the heads of many cell phone users today. For example, when you are checking out a new bag on the internet, all by yourself. The next thing you know, this bag follows you everywhere on the internet, like Facebook, online retailers and even LinkedIn. It is like this bag is stalking you. This is phenomenon is commonly known as targeted advertising (The Goodwill Community Foundation, 2019). What if the same principles of artificial intelligence and neural networks that are used for targeted advertising, could be used for recognizing patterns in the behaviour of millions of suicidal people.

 

As Brynjolfsson and McAfee (2017) state: artificial intelligence is changing the way we interact with data. Machines are really good in finding patterns in very large data sets, and they can make sense of those patterns much better and easier than humans can (Brynjolfsson and McAfee, 2017). Today, there is still a lot of stigma associated with mental illnesses, and this might be the reason for people to hesitate if they should consult others if they are struggling. The suicide rates are still increasing each year in the United States, even though there is a good way to recognize and reach the people who are struggling (Howard, 2019). There are a few common suicide warning signs, as Howard (2019) discusses in the video below.

The problem is that humans are not very good at detecting these patterns, like changes in someone’s sleep, exercise levels and public interaction. Artificial intelligence and deep learning can learn how to recognize these patterns (Brynjolfsson and McAfee, 2017). By, for example, tracking your social media habits, google searches and sleep data, artificial intelligence can recognize these warning signs and direct you to suicide prevention hotlines and websites. Would that not be amazing?

 

 

 

References 

Brynjolfsson, E. and McAfee, A. (2017). The Business Of Artificial Intelligence: What it can – and cannot – do for your organization. Harvard Business Review.

Howard, J. (2019). The US suicide rate is up 33% since 1999. [online] CNN. Available at: https://edition.cnn.com/2019/06/20/health/suicide-rates-nchs-study/index.html [Accessed 9 Oct. 2019].

The Goodwill Community Foundation. (2019). The Now: What is Targeted Advertising?. [online] Available at: https://edu.gcfglobal.org/en/thenow/what-is-targeted-advertising/1/ [Accessed 8 Oct. 2019].

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Albert Heijn, the Dutch Amazon Go?

24

September

2018

No ratings yet. Today, 24th of September 2018, Albert Heijn, the largest supermarket chain in The Netherlands, enabled cashier- and cash free shopping in two of their stores. With a ‘tap-to-go’ card or Android app their customers can scan the barcodes of the products they would like to buy. After 10 minutes the purchased amount will be withdrawn automatically from their linked bank account. This means the customer doesn’t need to wait in line for a cashier or spend time paying at a self-scan machine, thus will spend less time in the store.

This concept makes all of us think about the Amazon Go concept, but the technique Amazon uses is completely different compared to Albert Heijn’s solution. Amazon Go uses cameras with computer vision to scan which items has been taken from the shelf by which customer. The computer vision system is trained with deep learning technology, which enables the cameras to recognize the distinguishable patterns in products and people. Interestingly enough, the cameras also see when a customer puts a product back on the shelves, whilst not using facial recognition. When I first read about Amazon Go a few years ago, I thought that this futuristic idea would disrupt the retail industry and their industry leaders. Fortunately, I am proud to see that ‘our own’ Albert Heijn responded on time with a different technology, whilst still reaching the goal of consumer convenience.

Now, as a Business Information Management student, it is interesting to see what opportunities come with this way of cashier- and cash free shopping. I will start with the following possibility: if customers scan their products while standing before a shelf, the retailer could better understand customer in-store behaviour and monitor consumer traffic. This enables retailers to not only offer personalized discounts or promotions based on product preference, but even based on in-store behaviour. In the future, they could offer an extra discount for a product you just put back on the shelf or use in-store promotion screens who recognize your tap-to-go card and adjust their promotion to your preferences and the shelf you are standing at.

I am sure that Amazon and Albert Heijn didn’t use the only technologies who could enable cashier-and cash free shopping. Also, I believe that there are way more possibilities enabled by cashier-and cash free shopping. Let me know in the comments which technologies and opportunities you would relate to cashier-and cash free shopping!

Sources:

https://nos.nl/artikel/2251836-zonder-af-te-rekenen-de-supermarkt-uit-ah-begint-met-kassaloos-winkelen.html

https://dzone.com/articles/impact-of-big-data-analytics-in-retail-industry-te

https://www2.deloitte.com/content/dam/Deloitte/in/Documents/CIP/in-cip-disruptions-in-retail-noexp.pdf

https://www.wired.co.uk/article/amazon-go-seattle-uk-store-how-does-work

https://www.zdnet.com/article/amazon-go-heres-a-look-at-the-impact-on-human-jobs-retail-innovation-amazons-bottom-line/

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Elon Musk’s Open Source A.I. Easily Beats Professional Gamers at Largest eSports Tournament of the World

11

September

2017

4.67/5 (3)

During August 7-12, one of the largest tournaments in online gaming was held. The International 2017 was the seventh edition of an eSports championship for the popular online game Dota 2. Top players from all around the world competed for their share of the 24 million dollar prize pool. The tournament was attended by 20.000 fans and watched by hundreds of thousands online via Twitch.tv. The game is usually played in two teams of 5 players, and the winning team took home $10,862,683.

This year, at The International 2017, OpenAI* showed off their confidence in their machine-learning artificial intelligence software by creating a bot designed to play the game Dota 2 in a one versus one format. They gave the bot some very basic understanding of the game, receiving some feedback on what is ‘good’ and ‘bad’ in the game, after which the bot was released to practice and self-teach the game. Interestingly, the programmers didn’t have to give a lot of rules to the bot, because it can keep improving itself as it plays and practices against itself. Within a month, the bot went from laughably bad to achieving a superhuman skill level.

OpenAI then showcased the bot by letting it play against the top professional players, players who have put in more than 10,000 hours of practice in this particular game and play this game for a living. Not surprisingly, the bot won 26 matches against top players and even a world champion and lost only 2 times. The players admitted in an interview that the bot did things they never even thought of, but which they now also will incorporate in their own game since the strategies work so well. In this way, the bot became better than any human player, so pros now actually want to use the bot as a training partner to take their gaming skills to the next level.

It is very interesting to see how fast artificial intelligence can surpass human skill levels in certain settings with only minimal inputs. To see that after a few weeks, the bot reached a skill level which left the professional players in awe is really amazing. OpenAI hopes to keep improving the bot so they can eventually create a team of 5 bots which could play against pros or against each other. Of course, one could say that this is only a game, but I think that the OpenAI team did something very cool here, as they reached millions of people with this project, who might also get interested in AI and some of them might even discover applications for which AI and machine learning could be useful in our personal lives. There seems to be a great amount of possibilities within this field and I am excited to see the developments within AI over the next few years. As OpenAI say it:

“This is a step towards building AI systems which accomplish well-defined goals in messy, complicated situations involving real humans.”

*OpenAI (https://openai.com/) is a non-profit research start-up co-created by Elon Musk which has the goal of furthering the path to safe artificial intelligence. By developing and promoting safe artificial intelligence, they hope to benefit humanity as a whole. All of its patents and research are available to the public. If you are interested (and have some programming knowledge) you can even download free software from OpenAI’s website and create your own learning algorithm for any application you might think of.

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Halloween theme: Try out this AI powered algorithm generating scary imagery!

23

October

2016

No ratings yet. Just in time for Halloween MIT has started a project to generate scary imagery using an image processing algorithm based on artificial intelligence, specifically through deep learning. The algorithm can recognize and generate scary faces and make places look scarily. It is interesting to think about the purpose of this experiment, because can machines make us scared? The result is scarily accurate.

The image generation works through several steps. First it uses deep learning to generate completely new faces. Subsequently a filter with extra imagery is applied to scarify to the faces. After that the system learns from a voting system about the extent of scariness. The system that is behind this algorithm is based on unsupervised learning with convolutional networks (CNNs) and convolutional generative adversarial networks (DCGANs). If you want to read more about this, find the very interesting and recent conference paper by Chintala, Metz and Radford (2016) at https://arxiv.org/abs/1511.06434.

The cool thing is that you can help training the algorithm by going to http://nightmare.mit.edu/faces. After trying it myself I do not think that the machine still needs a big increment in learning anymore as the pictures already look quite scary. What do you think?

It is also interesting to think about the possibilities of this development in the future. Chintala, Metz and Redford (2016) imagine future applicabilities in video prediction as well as for audio. I imagine implications in for example virtual reality in which the environment is automatically generated with the intent to make you scared. The system could learn from how you react on the input and personalize the algorithm to make you even more scared.  Moreover just think about how this can be applied to theme parks to make those experiences even more immersive!

All in all this development is a step towards a scary but also very exciting future!

If you have any comments or other ideas for similar applications in artificial intelligence, let me know! If you want to read more about the algorithm go to http://nightmare.mit.edu.

http://boingboing.net/2016/10/23/using-machine-learning-to-auto.html

http://nightmare.mit.edu/faces

http://nightmare.mit.edu

https://arxiv.org/abs/1511.06434

Image from http://boingboing.net/2016/10/23/using-machine-learning-to-auto.html

 

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Is Artificial Intelligence Making Art?!

5

October

2016

4.6/5 (5) So, you’ve decided to read a blog about artificial intelligence making art, one of the activities that is considered impossible for computers to do due to the necessity of certain human cognitive traits that we have yet to understand ourselves? Well, without discussing the definition of art too much, I would like to tell you about the rapidly succeeding developments in the world of AI and how its applications are surprising scientists as they are becoming less dependent on human input and doing unprecedented complex tasks.

funny-animals (1)

To understand how pictures like the above are created with AI, we need to understand how artificial neural networks work.

Artificial neural networks make use of ‘nodes’ and are based on biological neural networks like you and I have. It is a hierarchy of these nodes and each node completes a very specific simple task, i.e. recognizing patterns and ‘firing’ a signal to a node higher up in the hierarchy when it does. For example, one node is specialized in recognizing the slash ( / ), for example in the letter A ( /-\ ). Another node is specialized in recognizing the backslash ( \ ), and when a node higher up in the hierarchy receives a signal of the slash,  ( / ), backslash ( \ ), and dash ( – ) nodes, it recognizes the letter A. In the same way, other letters are recognized and a few levels up in the hierarchy the nodes “Apple” or “cAr” are activated, depending on the other signals. The higher in the hierarchy, the more abstract these nodes become as the combination of more complex patterns increase.

Neuron3

The above is called deep learning and is part of the family of machine learning methods. These neural networks start ‘empty’ and are fed with incredible amounts of data, for example the whole google images catalog of cat pictures. Without supervision of you or me the program learns itself to distinguish a cat from a picture with a cat and a dog in it. Recognizing if a cat is a cat and not a dog is an example of a task that is effortless for humans, but has been extremely difficult for a piece of software to do.

Artificial intelligence is getting smarter. Not only are they telling us which movie to watch or what music to listen, recently there were AI programs that compiled a song, made a movie trailer, wrote a book, defeated the world champion in the Chinese game GO and won the TV show Jeopardy (the last two need a story of their own).

This brings us to the art that AI has been creating since a year. Researchers at Google realized that, after letting a artificial neural network learn, they could reverse the process. So instead of giving the program an image and asking what was on the image, they gave the program so called ‘white noise’, i.e. no object, and asked the program to create a picture of what it saw. As a result, the program started to look for patterns and created images of objects it ‘thought’ it saw, ending in images like these (there is a link behind the image with more of these).

Iterative_Places205-GoogLeNet_3 Iterative_Places205-GoogLeNet_4 Iterative_Places205-GoogLeNet_18iterative-lowlevel-feature-layer

Some people took it further and programmed the program to zoom into the picture it made, resulting in an infinite source of new patterns and new objects to create.

Deep_Dreaming_into_noise_with_inceptionism

AI is getting smarter as not only computing power but also techniques are improving. Researchers are getting unexpected output like the animated gif above and are surprised by the effectiveness of neural networks.

Although I think this is art, there are discussions on whether AI will ever succeed in human tasks like creating art. What do you think? Share your thoughts!

 

Joep Beliën

 

 

 

Wikipedia. (2016). Artificial neural network. [online] Available at: https://en.wikipedia.org/wiki/Artificial_neural_network [Accessed 4 Oct. 2016].

Newsweek. (2016). Can an artificially intelligent computer make art?. [online] Available at: http://europe.newsweek.com/can-artificially-intelligent-computer-make-art-462847?rm=eu [Accessed 4 Oct. 2016].

 Casey, M. and Rockmore, D. (2016). Looking for art in artificial intelligence. [online] Phys.org. Available at: http://phys.org/news/2016-05-art-artificial-intelligence.html [Accessed 4 Oct. 2016].

Wikipedia. (2016). Deep learning. [online] Available at: https://en.wikipedia.org/wiki/Deep_learning#Deep_neural_network_architectures [Accessed 4 Oct. 2016].

Furness, D. (2016). Google’s newly launched Magenta Project aims to create art with artificial intelligence. [online] Digital Trends. Available at: http://www.digitaltrends.com/cool-tech/ai-art-google-magenta-project/ [Accessed 4 Oct. 2016].

IFLScience. (2016). Google’s AI Can Dream, and Here’s What it Looks Like. [online] Available at: http://www.iflscience.com/technology/artificial-intelligence-dreams/ [Accessed 4 Oct. 2016].

Wikipedia. (2016). Jeopardy!. [online] Available at: https://en.wikipedia.org/wiki/Jeopardy! [Accessed 4 Oct. 2016].

Mordvintseev, A., Olah, C. and Tyka, M. (2015). Inceptionism: Going Deeper into Neural Networks. [online] Research Blog. Available at: https://research.googleblog.com/2015/06/inceptionism-going-deeper-into-neural.html [Accessed 4 Oct. 2016].

PBS Idea Channel, (2016). Can an Artificial Intelligence Create Art?. video] Available at: https://www.youtube.com/watch?v=Sbd4NX95Ysc [Accessed 4 Oct. 2016].

 

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