This week, what is a Chief Automation Officer, and why do you need one?
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Our team of experts has scoured the internet for the most exciting and informative articles so that you can stay up-to-date on Digital, Data, Blockchain, AI & Analytics, and Digital Transformation.
SEE: Algorithms as a Service: The Future of Computing
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Are You Making These Deadly Mistakes With Your AI Projects?
Too much of the wrong data, and not enough of the right data is killing AI projects
Since data is at the heart of AI, it should come as no surprise that AI and ML systems need enough good quality data to “learn”. In general, a large volume of good quality data is needed, especially for supervised learning approaches, in order to properly train the AI or ML system. The exact amount of data needed may vary depending on which pattern of AI you’re implementing, the algorithm you’re using, and other factors such as in house versus third party data. For example, neural nets need a lot of data to be trained while decision trees or Bayesian classifiers don’t need as much data to still produce high quality results.
So you might think more is better, right? Well, think again. Organizations with lots of data, even exabytes, are realizing that having more data is not the solution to their problems as they might expect. Indeed, more data, more problems. The more data you have, the more data you need to clean and prepare. The more data you need to label and manage. The more data you need to secure, protect, mitigate bias, and more. Small projects can rapidly turn into very large projects when you start multiplying the amount of data. In fact, many times, lots of data kills projects.
Strategic Management of Machine Learning Projects
12 Key guidelines to keep in mind before you superintend your next machine learning project
In this story, we’ll set out on a journey to explore 12 guidelines that effectively summarize Andrew Ng’s course on machine learning strategy which is known as “Structuring Machine Learning Projects”. This course covers a lot of information that often gets overlooked when studying the topic from any other source but that is quite crucial for getting your machine learning systems to work as efficiently and quickly as possible.
In this story we map such information to a set of guidelines that will help you make right decisions while managing machine learning projects and that will in advance answer a lot questions that would cross your mind otherwise in the process.
Whether you decide to consider this story as a précis of what’s to be learnt from the course or just lecture notes on it, it will probably work out. Just get your coffee, notebook and pencils ready and let’s get to action.
New Gartner survey: Only half of AI models make it into production
Everyone is building AI models, but production is harder
Automation and artificial intelligence (AI) are being broadly embraced by organizations even as multiple challenges remain – though the challenges may not be what many think.
Across multiple aspects of IT and AI, a lack of qualified IT professionals is often cited as a barrier to adoption. According to a new survey released by Gartner today, a lack of AI talent really isn’t an issue. A whopping 72% of organizations surveyed claimed they can either source or already have the AI talent they need.
Fear factor: Overcoming human barriers to innovation
Here’s how to create a culture that accounts for the human side of innovation.
Five years ago, Alex Honnold scaled the sheer face of the 3,000-foot El Capitan escarpment alone and without ropes—the only person to have ever done so. Honnold has great skill and discipline, but he is also blessed with a special brain: an MRI scan has shown that his brain doesn’t register fear.
Innovation may not put you at risk of sudden death, but it is anxiety inducing nonetheless. It is more ambiguous than any other business activity, requiring bold bets in the face of uncertain outcomes and a willingness to persevere despite setbacks, criticism, and self-doubt. Which is why most teams, in moments of honest self-reflection, will agree that fear can paralyze innovation. In fact, 85 percent of executives we recently polled agree that fear holds back innovation efforts often or always in their organizations.
Average or below-average innovators are three times more likely than innovation leaders to report this phenomenon. Yet nine out of ten organizations are doing nothing to allay these fears. In essence, they are counting on having Alex Honnolds among them to spearhead initiatives that others dare not attempt.
What Is CLIP and Why Is It Becoming Viral?
When a neural network uses so much data it becomes “universal”
Pre-defined classes and categories: this is the limitation where new classes can only be classified by machine learning and neural networks after retraining. For a period of time, this retraining and fine-tuning procedure have almost become “standard” — it is such a common practice that people forget that it is a still problem yet to be solved……at least before CLIP was introduced.
So, what exactly is CLIP?
CLIP (Contrastive Language-Image Pre-training) is a training procedure unlike common practices in the vision community. For a period of time, the capabilities of model/training methods are benchmarked on the ImageNet dataset that spans 1000 classes. We train on a subset of ImageNet, and test it on a different subset to measure how well a model generalises. While straightforward, this convention overlooks the exponentially scaling image collections on the internet and the potential benefits it could bring; CLIP, indeed, shows that it is a LOT we are missing out on.
Featured Article
What is a Chief Automation Officer, and Why do you need one?
IT operations and core business processes are ripe for transformation
Article by @VentureBeat
There definitely have been easier years than 2022 for trying to start a business. Compared to larger firms, smaller companies have a harder time absorbing shocks like inflation changes, supply chain disruptions, and changing demographics in the workplace. We see evidence that investors are starting to prefer to see proof of profits, rather than growth, an anathema to the startup founders of only a few years ago. At the same time, founders who embrace technological innovation have an immense opportunity.
Through our work with companies of all sizes across industries around the world, we see that the convergence of these trends explains the increased focus on “intelligent automation” as organizations embark on digital transformation journeys. By applying artificial intelligence (AI) to IT operations (AIOps), robotic process automation (RPA), decision management, and business automation, companies can reduce costs and do more with less. Intelligent automation also helps to combat the global skills shortage by allowing employees to work on more engaging, value-adding tasks, as well as helps companies deliver exceptional customer experiences.