This week, predicting probabilities – a clear guide.
Welcome to The Digital Eye, your weekly roundup of the latest technology news.
Our team of experts have scoured the internet for the most exciting and informative articles so that you can stay up-to-date on all things digital, data, blockchain, AI & analytics.
All over the world, governments, civil society, academia, industry, and citizens are trying to figure out the rapidly evolving technology changes occurring throughout society.
Whether you call it the 4th Industrial Revolution or Industry 4.0, or Society Next, only one thing is for sure; the impact of the technologies cannot be understated. Autonomous vehicles in the sky and on the ground, AI, Crypto, IoT, cloud computing, edge networks all share one similar trait; people worldwide are being challenged by rapid innovation occurring at a speed, scale, and scope never before seen.
They are challenged to regulate, to maintain the public interest, to support where they can and protect where they must. It is not to be taken lightly. Too much oversight and innovation falter, not enough, and you are left extremely vulnerable.
Article by @futurism
MIT Researcher: Don’t Ignore the Possibility That AI Is Becoming Conscious
“Seeing so many prominent [machine learning] folks ridiculing this idea is disappointing.”
Amid a maelstrom set off by a prominent AI researcher saying that some AI may already be achieving limited consciousness, one MIT AI researcher is saying the concept might not be so far-fetched.
Our story starts with Ilya Sutskever, the head scientist at the Elon Musk cofounded research group OpenAI. On February 9, Sutskever tweeted that “it may be that today’s large neural networks are slightly conscious.”
Article by @TechTarget
Is old data strategy drowning new data strategy?
Ted Gioia, author of Music: A Subversive History, made an excellent point in an article in The Atlantic titled “Is Old Music Killing New Music?” posted in January 2022:
“I learned the danger of excessive caution long ago, when I consulted for huge Fortune 500 companies. The single biggest problem I encountered—shared by virtually every large company I analyzed—was investing too much of their time and money into defending old ways of doing business, rather than building new ones. We even had a proprietary tool for quantifying this misallocation of resources that spelled out the mistakes in precise dollars and cents.
“Senior management hated hearing this, and always insisted that defending the old business units was their safest bet. After I encountered this embedded mindset again and again and saw its consequences, I reached the painful conclusion that the safest path is usually the most dangerous. If you pursue a strategy—whether in business or your personal life—that avoids all risk, you might flourish in the short run, but you flounder over the long term. That’s what is now happening in the music business.”
Article by @TechRepublic
Is the metaverse big data’s next big thing?
CIOs should plan ahead
The metaverse is still a futuristic technology for most companies, but it’s not too early to start planning your strategy.
Bloomberg estimates that the metaverse market may grow to $800 billion by 2024, and Facebook has changed its name to Meta to capitalize on this looming technology. The metaverse is a collection of immersive online technologies that include virtual reality, augmented reality and interactive video.
At the corporate level, CIOs are also taking note of metaverse, but most CIOs are adopting a wait-and-see approach, and instead are focusing their efforts on more deployments of artificial intelligence to aid in innovation and digitalization.
Article by @kdnuggets
Is Data Science a Dying Career?
At the end of the day, the value a data scientist provides to an organization lies in their ability to apply data to real-world use cases.
I recently read an article describing data science as an oversaturated field. The article predicted that ML engineers would replace data scientists in the upcoming years.
According to the author of this article, most companies worked to solve very similar business problems with data science. Due to this, it wouldn’t be necessary for data scientists to develop novel methods of solving problems.
The author went on to say that only basic data science skills were required to solve problems in most data-driven organisations. This role could easily be replaced by a machine learning engineer — a person with basic knowledge of data science algorithms and knowledge of deploying ML models.
Article by @TDataScience
You are capable of training and evaluating classification models, both linear and non-linear model structures. Well done! Now, you want class probabilities instead of class labels. Read no more. This is the article you are looking for. This article walks you through the different evaluation metrics, pros and cons and optimal model training for multiple ML models.
Classifying cats and dogs
Imagine creating a model with the sole purpose of classifying cats and dogs. The classification model will not be perfect and, therefore, wrongly classify specific observations. Some cats will be classified as dogs and vice versa. That’s life. In this example, the model classifies 100 cats and dogs. The confusion matrix is a commonly used visualisation tool to show prediction accuracy, and Figure 1 shows the confusion matrix for this example.