Companies are moving away from machine learning, quickly.
We see it every day; companies are leveraging the power of deep learning – that subset of A.I. based on neural networks that were designed to mimic how our brains learn and function. Deep learning models generally outperform classical machine learning methods. Drastically. There are quite a few good reasons for this, and I will discuss some of them here, along with the advances we’ve made for our clients over the past few years.
Where anything related to image processing is concerned, we can often use transfer learning to extract deep knowledge from images. Companies have released pre-trained deep learning models to the public. These models were trained on millions of examples and have “learned” an incredible amount about what’s in digital images. We use a technique called transfer learning to piggyback off this work, and it’s not only useful for images. We can also use pre-trained neural nets for natural language processing.
I’ve written here about the sometimes scary power of the biggest text models, like GTP-2. We can no longer tell if a human or machine wrote a block of text. Using classical machine learning methods such as latent semantic analysis (LSA) is still a powerful technique for text processing, but when the application allows, leveraging the deep power of the newest language models can provide that magic that standard machine learning methods cannot.
We’ve used image similarities for our clients to successfully search through millions of images to find duplicates or similar images. And we can do it in only a few milliseconds. This is because we no longer need to think of images as these large matrices of red, blue and green color mappings. Using transfer learning, we reduce large complex images to thousands or hundreds of numbers. Computers are quite good at handling arrays of numbers this way. This simplification allows us great freedom to compare images quickly and at scale.
We built our text-based travel recommender (TRecs) atop the Bert deep learning language model as well as the LSA method mentioned above. It provides incredible fidelity, leveraging deep destination knowledge from our extensive use of transfer learning applied to our travel problem. We are able to search through thousands of destinations and identify the top 50 in only a few milliseconds.
As your company gets into A.I., are you using classical machine learning methods or have you started to leverage neural networks for their power? Neural networks aren’t appropriate to use for every case, but gone are the days when using their incredible power required weeks or months of training on millions of labeled examples, costing hundreds of thousands of dollars.
I recommend assessing your use of A.I. and speaking with someone familiar with neural networks to see if you may be missing a lot of potential upside. We’ve employed deep learning models for many recent clients, across all the major cloud platforms with fantastic success. We’ve seen this innovative technology help companies increase engagement, personalization and drive revenue. Don’t be left behind.
From Google – A Simulation Platform for Recommenders
Significant advances in machine learning, speech recognition, and language technologies are rapidly transforming the way in which recommender systems engage with users. As a result, collaborative interactive recommenders (CIRs) — recommender systems that engage in a deliberate sequence of interactions with a user to best meet that user’s needs — have emerged as a tangible goal for online services. https://ai.googleblog.com/2019/11/recsim-configurable-simulation-platform.html
Here’s why so Many Data Scientists are Leaving Their Jobs
There are many reasons why data scientists leave their jobs. For example, many companies hire data scientists without a suitable infrastructure in place to start getting value out of AI. This contributes to the cold start problem in AI. Couple this with the fact that these companies fail to hire senior/experienced data practitioners before hiring juniors, you’ve now got a recipe for a disillusioned and unhappy relationship for both parties. Read more here: https://towardsdatascience.com/why-so-many-data-scientists-are-leaving-their-jobs-a1f0329d7ea4
The 5 Most Useful Techniques to Handle Imbalanced Datasets
Have a classification problem where you need to train a model to detect an event, say, churn. But most of your customers (lucky you) don’t churn. What do you do when most of your training data contains non-events (such as a churn)? This article dives into this problem, called class imbalance and gives some ideas for handling it. Read more here: https://towardsdatascience.com/the-5-most-useful-techniques-to-handle-imbalanced-datasets-6cdba096d55a