The day ChatGPT outstripped its limitations for Me

20

October

2023

No ratings yet.

We all know ChatGPT since the whole technological frenzy that happened in 2022. This computer program was developed by OpenAI using GPT-3.5 (Generative Pre-trained Transformer) architecture. This program was trained using huge dataset and allows to create human-like text based on the prompts it receives (OpenAI, n.d.). Many have emphasized the power and the disruptive potential such emerging technology has whether it be in human enhancement by supporting market research and insights or legal document drafting and analysis for example which increases the efficiency of humans (OpenAI, n.d.).

Hype cycle for Emerging Technologies retrieved from Gartner.

However, despite its widespread adoption and the potential generative AI has, there are still many limits to it that prevent us from using it to its full potential. Examples are hallucinating facts or a high dependence on prompt quality (Alkaissi & McFarlane, 2023; Smulders, 2023). The latter issue links to the main topic of this blog post.

I have asked in the past to ChatGPT, “can you create diagrams for me?”  and this was ChatGPT’s response:

I have been using ChatGPT for all sorts of problems since its widespread adoption in 2022 and have had many different chats but always tried to have similar topics in the same chat, thinking “Maybe it needs to remember, maybe it needs to understand the whole topic for my questions to have a proper answer”. One day, I needed help with a project for work in understanding how to create a certain type of diagram since I was really lost. ChatGPT helped me understand but I still wanted concrete answers, I wanted to see the diagram with my own two eyes to make sure I knew what I needed to do. After many exchanges, I would try again and ask ChatGPT to show me, but nothing.

One day came the answer, I provided ChatGPT with all the information I had and asked again; “can you create a diagram with this information”. That is when, to my surprise, ChatGPT started creating an SQL interface, representing, one by one, each part of the diagram, with the link between them and in the end an explanation of what it did, a part of the diagram can be shown below (for work confidentiality issues, the diagram is anonymized).

It was a success for me, I made ChatGPT do the impossible, something ChatGPT said itself it could not provide for me. That day, ChatGPT outstripped its limitations for me. This is how I realized the importance of prompt quality.

This blog post shows the importance of educating the broader public and managers about technological literacy in the age of Industry 4.0 and how with the right knowledge and skills, generative AI can be used to its full potential to enhance human skills.

Have you ever managed to make ChatGPT do something it said it couldn’t with the right prompt? Comment down below.

References:

Alkaissi, H., & McFarlane, S. I. (2023). Artificial hallucinations in ChatGPT: implications in scientific writing. Cureus, 15(2).

Smulders, S. (2023, March 29). 15 rules for crafting effective GPT Chat prompts. Expandi. https://expandi.io/blog/chat-gpt-rules/

Please rate this

How Spotify knows what you like

10

October

2021

5/5 (3)

Remember the time where we needed to download music on our computers and transfer those songs to our mp3, not being able to listen to more than 20 songs? Times have changed since then, and almost everyone now uses the app Spotify. Hundreds of millions of people around the world use Spotify to listen to their music. With over 50 million songs and podcast episodes, it is not surprisingly beating the mp3 (;

But Spotify is doing more than just giving people access to podcasts and artists and their albums; Spotify is using technology to give their users an exceptional personal experience.

For example, they brought in Discover weekly, where every monday players receive a new playlist with 50 tracks. This playlist is based on songs they like and recently listened to, but haven’t heard before. How does Spotify do this? They use a form of machine learning. One of the used techniques is Collaborative Filtering, where an algorithm compares the songs you’ve listened to with other user-created playlists with similar songs. Another technique that uses a similar algorithm, but in a different way is Natural Language Processing (NLP). NLP is the ability of an algorithm to search through the web to find music related articles and blog posts. This way, the algorithm can match songs based on the way they are being discussed on the internet and new songs can be added to the discover weekly list. Additionally, they use Convolutional Neural Networks (CNN) to make sure also less-popular songs are considered for the playlist. With CNN Spotify matches songs based on their attributes (e.g. beats per minute, loudness).

The company also just released a new feature on the 9th of September called ‘Enhance’. With this feature Spotify adds recommended tracks to your own playlist based on the already existing tracks using similar algorithms as described above. 

All these techniques make it easier for us to discover new artists and tracks more than ever. Algorithms track what we like, then give us what they think we like. This also made me think how hard it has become to discover new types of music that differs from what we already know. I still have a record player in my room and go to record stores every once in a while to step out of this filer bubble and wander into fresh territory. How about you?

References

E. (2021, January 15). On Netflix and Spotify, algorithms hold the power. But there’s a way to get it back. Experience Magazine. https://expmag.com/2019/11/endless-loops-of-like-the-future-of-algorithmic-entertainment/

How Spotify Uses Artificial Intelligence, Big Data, and Machine Learning. (2021). Data Science Central. https://www.datasciencecentral.com/profiles/blogs/6448529:BlogPost:1041799

Tambekar, A. (2020, May 11). How Spotify Uses Machine Learning Models to Recommend You The Music You Like. GreatLearning Blog: Free Resources What Matters to Shape Your Career! https://www.mygreatlearning.com/blog/3-machine-learning-models-spotify-uses-to-recommend-music-youll-like/#:%7E:text=Convolutional%20Neural%20Networks&text=Each%20song%20is%20converted%20into%20a%20raw%20audio%20file%20as%20a%20waveform.&text=With%20these%20key%20machine%20learning,would%20have%20never%20found%20otherwise.

Please rate this

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.

No ratings yet.

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

Please rate this

Author: Roël van der Valk

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

How obsolete can humankind become?

6

October

2021

No ratings yet.

The rapid evolution of automation, machine learning and AI has prompted a lot of questions regarding humankinds’ future. Will machines be able to take over any job? Is my job safe? Are we looking at a society with above 50% unemployment? What other impacts will this have on humans? What are the challenges that come with “robots”?

The development has ignited governments to rethink. In 2016, the Swiss went to the voting machines to vote over income to all citizens regardless of employment. The result? A solid “No”. The swiss government’s argument was that if machines were to take more and more jobs, the unemployment rate would increase. In a society, the economy is reliant on sellers and buyers. With such a high rate of unemployment, fewer people would be able to buy, leading to a decrease in the flow of money. Which would inevitably damage the economy and society. The argument behind the proposed change becomes more and more relevant as AI and machines improve. However, is this a good solution? Could that lead to a “Wall-E” type world where all you do is “chill” day-in and day-out?  

“I do not find this a promising future, as I do not find the prospect of leisure-only life appealing. I believe that work is essential to human well-being”. – Moshe Vardi on machines substituting humans.

Moshe Vardi, a computer scientist at Rice university, argues that the negative projections of human labor would be highly damaging to society. The continuously difficult question “what is the meaning of life” is today answered through working. Working hard to achieve things brings out a satisfaction of accomplishment. With a lot of middle-class jobs on the brink of being replaced more people would find less value in life. Vardi, argues that this is humankinds’ biggest challenge yet. How do we coexist with machines? Could humans find another purpose than working?

Of course, machines are not going to overtake every job on the market right away. The development takes time and most likely humans are going to be required for certain jobs. However, anticipating the future is impossible. Which means it is even more important to think about the possibilities and challenges it might hold. It could be an idea to venture outside and start focusing on alternatives to working or reshape how we look at working. What are your thoughts on the matter? How obsolete can humankind become?

Sources:

Leetaru, K. (2016). Will AI and Robots make humans obsolete? Forbes. Retrieved from https://www.forbes.com/sites/kalevleetaru/2016/06/14/will-ai-and-robots-make-humans-obsolete/?sh=d51b45d35f2c

Rice University. (2016). When machines can do any job, what will humans do? Human labor may be obsolete by 2045. ScienceDaily. Retrieved from www.sciencedaily.com/releases/2016/02/160213185923.htm

Please rate this

How Greece used AI to detect asymptomatic travelers infected with COVID-19

29

September

2021

No ratings yet.


A few months after the Covid-19 outbreak, operations researcher Kimon Drakopoulos, who works in data science at the University of Southern California, offered to help the Greek government by developing a system that uses machine learning in order to determine which travelers had the most risk of being infected and thus should get tested. Greece was asked by the European Union to allow non-essential travel again, but of course the option of testing all travelers was not available. Consequently, they chose to implement a more efficient way to test incoming travelers than the usual practices of randomized sample testing or testing based on the visitor’s country of origin, by launching this system called ‘Eva’ and deploying it across all Greek borders.

Drakopoulos and his colleagues discovered that machine learning proved to be more effective at identifying asymptomatic cases than the aforementioned methods, by a factor of two to four times during peak tourist season. This was accomplished because Eva used multiple sources of data, besides just travel history, to assess and estimate the infection risk of an individual. These sources include demographic data like the age and sex of the travelers, which was then paired with the obtained data from previously tested passengers, to calculate who had the highest risk out of a group and needed to be tested. This process was also used to provide information to the border policies about real-time estimates of the prevalence of COVID-19.

When the researchers compared the performance of this model against the methods that only use epidemiological metrics, such as random testing, it was clear that it performed better in all aspects. One main reason for this was the limited predictive value that these metrics possessed in relation to asymptomatic cases. Consequently, the paper raises concern on the effectiveness of internationally proposed border policies that employ such population-level metrics.

All in all, Eva is a successful example of how the use of reinforcement learning and artificial intelligence in combination with real-time data can provide very useful assistance both in crisis situations but also in the public health sector.

References

Bastani, H., Drakopoulos, K., Gupta, V. et al. Efficient and targeted COVID-19 border testing via reinforcement learning. Nature (2021). https://doi.org/10.1038/s41586-021-04014-z

Nature (2021) ‘Greece used AI to curb COVID: what other nations can learn’, 22 September. Available at: https://www.nature.com/articles/d41586-021-02554-y  (Accessed: 29 September 2021).

Please rate this

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)

Please rate this

BIM, Meet Gertrude!

6

October

2020

Gertrude enjoying a well deserved drink during her performance. 

In August 2020, famous tech entrepreneur Elon Musk revealed his latest technological project: a pig called Gertrude. On first sight, Gertrude looks like an ordinary Pig. She seems healthy, curious, and eager to taste some delicious snacks. When looking at her, it is hard to imagine how she managed to get one of the world’s most radical and well known tech entrepreneurs so excited. Gertrude just seems normal.

This is exactly the point!

ElonMuskGotcha

Elon Musk “Gotcha”

Gertrude is no ordinary pig. She has been surgically implanted with a brain-monitoring chip, Link V0.9, created by one of Elon Musk’s latest start-ups named Neuralink.

Neuralink was founded in 2016, by Elon Musk and several neuroscientists. The short term goal of the company is to create devices to treat serious brain diseases and overcome damaged nervous systems. Our brain is made up of 86 billion neurons: nerve cells which send and receive information through electrical signals. According to Neuralink, your brain is like electric wiring. Rather than having neurons send electrical signals, these signals could be send and received by a wireless Neuralink chip.

To simplify: Link is a Fitbit in your skull with tiny wires

The presentation in August was intended to display that the current version of the Link chip works and has no visible side-effects for its user. The user, in this case Gertrude, behaves and acts like she would without it. The chip is designed to be planted directly into the brain by a surgical robot. Getting a Link would be a same day surgery which could take less than an hour. This creates opportunities for Neuralink to go to the next stage: the first human implantation. Elon Musk expressed that the company is preparing for this step, which will take place after further safety testing and receiving the required approvals.

The long term goal of the Neuralink is even more ambitious: human enhancement through merging the human brain with AI. The system could help people store memories, or download their mind into robotic bodies. An almost science-fictional idea, fuelled by Elon Musk’s fear of Artificial Intelligence (AI). Already in 2014, Musk called AI “the biggest existential threat to humanity”. He fears, that with the current development rate, AI will soon reach the singularity: the point where AI has reached intelligence levels substantially greater than that of the human brain and technological growth has become uncontrollable and irreversible, causing unforeseeable effects to human civilization. Hollywood has given us examples of this with The Matrix and Terminator. With the strategy of “if you cannot beat them, join them”, Elon Musk sees the innovation done by Neuralink as an answer to this (hypothetical) catastrophical point in time. By allowing human brains to merge with AI, Elon Musk wants to vastly increase the capabilities of humankind and prevent human extinction.

Singularity
Man versus Machine

So, will we all soon have Link like chips in our brains while we await the AI-apocalypse?

Probably not. Currently, the Link V0.9 only covers data collected from a small number of neurons in a coin size part of the cortex. With regards to Gertrude, Neuralink’s pig whom we met earlier in this article, this means being able to wirelessly monitor her brain activity in a part of the brain linked to the nerves in her snout. When Gertrude’s snout is touched, the Neuralink system can registers the neural spikes produced by the neurons firing electronical signals. However, in contrast: major human functions typically involve millions of neurons from different parts of the brain. To make the device capable of helping patients with brain diseases or damaged nervous system, it will need to become capable of collecting larger quantities of data from multiple different areas in the brain.

On top of that, brain research has not yet achieved a complete understanding of the human brain. There are many functions and connections that are not yet understood. It appears that the ambitions of both Elon Musk and Neuralink are ahead of current scientific understanding.

So, what next?

Neuralink has received a Breakthrough Device Designation from the US Food and Drug Administration (FDA), the organisation that regulates the quality of medical products. This means Neuralink has the opportunity to interact with FDA’s experts during the premarket development phase and opens the opportunity towards human testing. The first clinical trials will be done on a small group of patients with severe spinal cord injuries, to see if they can regain motor functions through thoughts alone. For now a medical goal with potentially life changing outcomes, while we wait for science to catch up with Elon Musk’s ambitions.

 Neuralink-Logo

Thank you for reading. Did this article spark your interest?
For more information, I recommend you to check out Neuralink’s website https://neuralink.com/

Curious how Gertrude is doing?
Neuralink often posts updates on their Instagram page https://www.instagram.com/neura.link/?hl=en

Want to read more BIM-articles like this?
Check out relating articles created by other BIM-students in 2020:

Sources used for this article:

4.88/5 (8)

Please rate this

From Data Analytics to Results on the Pitch

5

October

2020

5/5 (1) On football shows such as Match of the Day, well-known pundits commonly let their sentiments be heard on the recent performances of certain players and clubs. After all, who doesn’t love to tune in on Jamie Carragher and Phil Neville arguing over Manchester United’s loss of form? While much of what is said about a player’s performance is an opinion, these accusations as well as glorifications are almost always supported by data, presented in the form of statistics. It is no surprise that data collected on a player’s total distance covered, shot conversion, and pass completion may be used to bolster these arguments, as this has been common throughout the past decade.

Recently, however, the value of data within the context of football has significantly risen, due to developments in deep learning and predictive analytics (Murray & Lacome, 2019). Adapted training sessions, player recruitment, and analysis of the opponent’s playing style are all ways in which clubs’ staff can improve their decision making by leveraging data.

Although from a fan’s perspective most of the football action takes place on game day, according to Murray and Lacome (2019), professional players train at least five days a week. Data is constantly collected on a variety of player metrics, such as running distance and number of accelerations, as well as force load distribution. Trackers that collect this data help prepare the intensity of certain drills. Analyzing the force load distribution, for example, allows coaches to examine which of a player’s muscle groups are weak, and therefore critical decisions can be made leading up to the day of the match.

Furthermore, data collected on a team and its opponents have proven to provide valuable insights. According to Burn-Murdoch (2018), football’s “analytics era” began in 2006, when London-based Opta Sports recorded the time and location of every pass, shot, tackle, and dribble. Today, about 2,000 data points are collected per match (Burn-Murdoch, 2018). This development in data collection has progressed to the point where Premier League shows such as Match of the Day now present viewers with the number of goals they can expect that weekend.

However, arguably the most impressive development in data-driven football, has come from sports scientists that have developed algorithms that predict the likeliness of certain in-game player decisions (Burn-Murdoch, 2018). As shown by the depiction below, machine learning programs are now able to determine player movements and the amount of space a player consequently creates by their positioning on the pitch. This technique, referred to as “ghosting”, has as a result uncovered an otherwise difficult-to-uncover aspect of a player’s skill set, namely creating space, which is an invaluable asset when considering buying a player.

Considering the impact data analytics has already had in the football world within the last decade, who knows which new technological developments will occur in the near future and how they will shape the way decisions are made!

References:

Murray, E. and Lacome, M., 2019. What Difference Can Data Make To A Football Team?. [online] Exasol. Available at: <https://www.exasol.com/en/what-difference-can-data-make-for-a-football-team/> [Accessed 5 October 2020].

Burn-Murdoch, J., 2018. How Data Analysis Helps Football Clubs Make Better Signings. [online] Financial Times. Available at: <https://www.ft.com/content/84aa8b5e-c1a9-11e8-84cd-9e601db069b8> [Accessed 5 October 2020].

Please rate this

Your Profile Is Being Scraped

18

September

2020

4.33/5 (3) Facial recognition is gaining interest the last few years, all around the internet and also on this forum, more and more is being written about facial recognition itself, the positive and negative effects and the underlying technologies. Major companies are competing on developing better algorithms and are selling their developed technologies as cloud services. Easy API’s make it possible for every tech savvy person to use those services within minutes. But still the subject of facial recognition is still a lot of theory and less action. Current news items often discussed a few local tests or the implementation of video tracking within law enforcements. The major steps made on facial recognition are made within China, were facial identification or payment becomes more mainstream. But over the last year one company’s name popped up several times, gaining interest of several tech journalist, Clearview AI.

A lot of people nowadays have a certain social media profile, often with a public name, profile picture and some basic information. Of course it would be possible to go to every page and collect user information randomly, but no one every took the time to do this or saw the benefits of doing this, expect the startup Clearview AI.

Scraping is the act of automatically extracting public data of the internet. Every website can be scraped, even all data and texts from this blog for example. Clearview AI, performed these scraping operations on a huge level, they started scraping all the public profiles of Facebook and saved this data in one big database. If your profile picture and name are public on one of your social media accounts, which are probably most of the profiles, it is likely that these are included in the database of Clearview AI.

Would not every law enforcement agency be interested in the possibility of finding a suspect with the help of a few clicks? Robbers, fraudsters or cyber bullies are also people, most of the time with a personal social media account. This is exactly what Clearview AI thought while developing their business model, by scraping all public available data, training huge neural networks and selling it worldwide all bundled in a good looking application to law enforcement agencies. According to a graph of the New York Times, this will bring the number of photos the FBI can search from their own database of 411 million photos to a staggering number of 3 billion photos that are included in the Clearview AI application, all supported by an impressive artifical intelligence model.

This brings up some important questions, do we support facial recognition as a way of law enforcement? Is it legal to scrape information from social networks? Does making your profile public also implies that you give permission for your data to be saved and used for AI training purposes?

Next to the negative sides of web scraping, there are also interesting possibilities of using these methods. You could for example scrape this blog and analyze the word usage or identify trends and topics of interest over time. Web scraping also enables new innovations that aggregate data from multiple sources in creative ways creating information that was not available before.

The New York Times has an article going more into depth in the background of Clearview AI. Click here to read the full article or listen to accompanying podcast if your interested.

I would love to hear your opinion about the subject of web scraping and the usage of facial recognition. If you like to have a more technical background on how to implement web scraping techniques please let me know in the comments.

 

Sources

Hill, K. (2020, January 18). The Secretive Company That Might End Privacy as We Know It. The New York Times. https://www.nytimes.com/2020/01/18/technology/clearview-privacy-facial-recognition.html

Matsakis, L. (2020, January 27). Scraping the Web Is a Powerful Tool. Clearview AI Abused It. Wired. https://www.wired.com/story/clearview-ai-scraping-web/

 

 

Please rate this

Sports and Technology. Yes or No?

18

October

2019

5/5 (2) Sports has been one of many entertainments option for the world’s population; since the gladiator era until the rise of eSports. Now, the world is modernizing through data and analytics. How will this development change the sports that we have known for long?

What changes?

Indeed, technology development has modernized the sports that we know. Technologies such as virtual reality promises sport enthusiasts of an immersive experience on watching sports. Users can now enjoy the stadium atmosphere on their couch, thanks to the VR-headset that they use whilst watching the game from their cable TV (Pierce, 2019). Furthermore, the sports league is also trying to bring more personalization to the users. ESPN, for example, provide users the option to choose their favorite broadcaster to comment the game for them (Pierce, 2019). As such, I think, these technologies might change the way fans watch sports matches. But is that it?

The answer is no. And that is because we have this rising technology called (buzzword alert!) machine learning. The technology offers interesting applications for sports team, particularly, around prediction domain (Warner, 2019). With machine learning sports team can predict, for example, winning probability and player performance, provided enough historical data exists. The English Premier League, for example, use machine learning to predict the direction of opponents’ penalty shot, using historical data, to help the goalkeeper make better decision (Morgulev, et al., 2018). Likewise, in US’ Major League Soccer, machine learning has been used to recommend the optimal game plan to coaches (Barlas, 2014). Another example will be the National Football League (NFL), who are using machine learning to understand the best route to run the ball (Lemire, 2019). Furthermore, the NFL has also put sensors in the players shoulder pads, therefore, allowing the player stats to be tracked continuously (Proman, 2019).

Better or Worse?

All in all, sports have and will change; be it on the way fans watch the matches or teams adjust their game plans. Fans no longer need to go the the stadium miles away to watch the game, they can simply put on a VR-goggle and enjoy the stadium atmosphere. Coaches no longer needs to assess the fitness of the players, as sensors already record their diets and fatigue (Barlas, 2014). Players no longer need to watch game films as extensive data analytics will provide them with their opponents favorite moves or ’hotspots’.

The question is, does it change sports for the better or for the worse?

References

Barlas, P., 2014. Data Analytics Get In The Sports Game Soccer, football teams turn to wearable tech, software for big wins. Investor’s Business Daily , Volume A01, p. 1.

Lemire, J., 2019. Sport Techie. [Online] Available at: sporttechie.com/nfl-big-data-bowl-running-backs-michael-lopez-analytics-director-football [Accessed 16 October 2019].

Morgulev, E., Azar, O. H. & Lidor, R., 2018. Sports analytics and the big-data era. International Journal of Data Science and Analytics, 5(4), pp. 213-222.

Pierce, D., 2019. The Wallstreet Journal. [Online] Available at: https://www.wsj.com/articles/the-future-of-sports-is-interactive-immersive-and-intense-11552827600 [Accessed 18 October 2019].

Proman, M., 2019. Techcrunch. [Online] Available at: https://techcrunch.com/2019/10/01/the-future-of-sports-tech-heres-where-investors-are-placing-their-bets/ [Accessed 1 October 2019].

Warner, J., 2019. Customer Think. [Online] Available at: https://customerthink.com/sports-and-machine-learning-is-there-a-connection/ [Accessed 16 October 2019].

 

Please rate this