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In aglobal reportissued by S&P, 95% of enterprises across various industries said that Artificial Intelligence (AI) adoption is an important part of their digital transformation journey. Were seeing expanded interest in the adoption of AI for many reasons, including lowering costs, increasing sales, and improving worker productivity. At the same time, if youre keeping up with the news on AI these days, you know were also seeing considerable focus placed on explaining how AI models work and why explainability is important. But our question as two AI practitioners Is explainability that important? Or does it lead to a false sense of security?

Explainable Artificial Intelligence (XAI), as summed up by IBM Watson, is a set of processes and methods that allows human users to comprehend and trust the results and outputcreated by machine learning algorithms.Many believe that XAI promotes model transparency and trust, making people more comfortable with the risk of improper learning and incorrect predictions that can occur with machine learning models.

Its human nature to seek explanations as a means of better understanding unknown subjects. We lean on explainability even more when the stakes are high. Asrecently concluded by two Dartmouth researchers, if the explanation is visually supported by pretty charts, we are partial to it. Explanations can give us a feeling of security when it comes to making informed decisions. Take, for example, a patient who asks a doctor for an explanation of a diagnosis. Even when the explanation is hard to grasp, the more scientific the doctor sounds, the better the patient may feel. It can be the same with AI. The more detail end users are given about how it works, the more likely they are to accept the outcome as valid and feel confident about doing so.

Are explanations sufficient? Some things are complex, and merely having an explanation is not a sufficient and necessary condition to derive utility.

And with many businesses considering avenues for AI adoption, we have to ask about the risks associated with relying so heavily on explainability. What if the explainer is not sufficiently knowledgeable? Users could be fed incorrect information without realizing it. What if theres not familiarity with the topic to fully grasp the explanation? It is quite possible that when it comes to new topics like AI models, users such as business stakeholders, regulators, and even domain experts may end up with only a superficial understanding of the explanation provided. They may not be able to discern if and how the model was incorrect in the first place, which means even with explanations, users can still end up with disastrous decision making.

In many use cases, a more accurate model is better than having an explanation. After all, what better evidence of utility than a model that gives the right outcome? Hence, we must question if we should be going after explainability, as is the rage right now in XAI, or after truthfulness?

Truthfulness comes from accuracy measures, which give us an indication of how much reliance we can place on the system. Accuracy is directly linked to the quality of the underlying data. The progression of data quality and accuracy over time goes hand and hand. Many AI models are used in dynamic settings where data drift is the norm. Asking crucial questions about the distribution of training data and out of sample data is elemental to having accurate models that can be relied on.

Forget explanations and reasoning for a moment and picture a system that can establish a high degree of truthfulness by means of doing well on a large test dataset across different real-world distributions. Seems too good to be true, right?

Let us examine this concept using a real-life scenario. Have you ever had to ask your colleague or friend for an explanation of how they recognized you in just a nanosecond of time? No, because of the truthfulness of the outcome. It never crosses your mind to understand the how, because the end result is correct with a high degree of accuracy. Similarly in AI, when we transition to a phase where the models accuracy beats the human baseline, and we reach that high degree of accuracy, explainability will become less relevant. So, what is the alternative to explainability? Simplified, business-friendly metrics. As AI practitioners, we need to recognize that it is difficult for non-practitioners to make sense of our different analytical metrics, such as: F1 Score, Rouge Score, Perplexity, Bleu Score, WER, Confusion Matrix, etc. We need a simplified, business-friendly metric that can be readily understood, like Googles use of Sensibleness and Specificity Average (SSA) Score in their evaluation score for Meena.[1]While it may not be easy to develop simplified metrics in all instances, its imperative we do so whenever possible to limit the need for model explanations and ultimately lead to better decision-making for AI end users.

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The illusion of explainability in machine learning models - Finextra

Its no secret that OpenAIs ChatGPT has some incredible capabilities for instance, the chatbot can write poetry that resembles Shakespearean sonnets or debug code for a computer program. These abilities are made possible by the massive machine-learning model that ChatGPT is built upon. Researchers have found that when these types of models become large enough, extraordinary capabilities emerge.

But bigger models also require more time and money to train. The training process involves showing hundreds of billions of examples to a model. Gathering so much data is an involved process in itself. Then come the monetary and environmental costs of running many powerful computers for days or weeks to train a model that may have billions of parameters.

Its been estimated that training models at the scale of what ChatGPT is hypothesized to run on could take millions of dollars, just for a single training run. Can we improve the efficiency of these training methods, so we can still get good models in less time and for less money? We propose to do this by leveraging smaller language models that have previously been trained, says Yoon Kim, an assistant professor in MITs Department of Electrical Engineering and Computer Science and a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL).

Rather than discarding a previous version of a model, Kim and his collaborators use it as the building blocks for a new model. Using machine learning, their method learns to grow a larger model from a smaller model in a way that encodes knowledge the smaller model has already gained. This enables faster training of the larger model.

Their technique saves about 50 percent of the computational cost required to train a large model, compared to methods that train a new model from scratch. Plus, the models trained using the MIT method performed as well as, or better than, models trained with other techniques that also use smaller models to enable faster training of larger models.

Reducing the time it takes to train huge models could help researchers make advancements faster with less expense, while also reducing the carbon emissions generated during the training process. It could also enable smaller research groups to work with these massive models, potentially opening the door to many new advances.

As we look to democratize these types of technologies, making training faster and less expensive will become more important, says Kim, senior author of a paper on this technique.

Kim and his graduate student Lucas Torroba Hennigen wrote the paper with lead author Peihao Wang, a graduate student at the University of Texas at Austin, as well as others at the MIT-IBM Watson AI Lab and Columbia University. The research will be presented at the International Conference on Learning Representations.

The bigger the better

Large language models like GPT-3, which is at the core of ChatGPT, are built using a neural network architecture called a transformer. A neural network, loosely based on the human brain, is composed of layers of interconnected nodes, or neurons. Each neuron contains parameters, which are variables learned during the training process that the neuron uses to process data.

Transformer architectures are unique because, as these types of neural network models get bigger, they achieve much better results.

This has led to an arms race of companies trying to train larger and larger transformers on larger and larger datasets. More so than other architectures, it seems that transformer networks get much better with scaling. Were just not exactly sure why this is the case, Kim says.

These models often have hundreds of millions or billions of learnable parameters. Training all these parameters from scratch is expensive, so researchers seek to accelerate the process.

One effective technique is known as model growth. Using the model growth method, researchers can increase the size of a transformer by copying neurons, or even entire layers of a previous version of the network, then stacking them on top. They can make a network wider by adding new neurons to a layer or make it deeper by adding additional layers of neurons.

In contrast to previous approaches for model growth, parameters associated with the new neurons in the expanded transformer are not just copies of the smaller networks parameters, Kim explains. Rather, they are learned combinations of the parameters of the smaller model.

Learning to grow

Kim and his collaborators use machine learning to learn a linear mapping of the parameters of the smaller model. This linear map is a mathematical operation that transforms a set of input values, in this case the smaller models parameters, to a set of output values, in this case the parameters of the larger model.

Their method, which they call a learned Linear Growth Operator (LiGO), learns to expand the width and depth of larger network from the parameters of a smaller network in a data-driven way.

But the smaller model may actually be quite large perhaps it has a hundred million parameters and researchers might want to make a model with a billion parameters. So the LiGO technique breaks the linear map into smaller pieces that a machine-learning algorithm can handle.

LiGO also expands width and depth simultaneously, which makes it more efficient than other methods. A user can tune how wide and deep they want the larger model to be when they input the smaller model and its parameters, Kim explains.

When they compared their technique to the process of training a new model from scratch, as well as to model-growth methods, it was faster than all the baselines. Their method saves about 50 percent of the computational costs required to train both vision and language models, while often improving performance.

The researchers also found they could use LiGO to accelerate transformer training even when they didnt have access to a smaller, pretrained model.

I was surprised by how much better all the methods, including ours, did compared to the random initialization, train-from-scratch baselines. Kim says.

In the future, Kim and his collaborators are looking forward to applying LiGO to even larger models.

The work was funded, in part, by the MIT-IBM Watson AI Lab, Amazon, the IBM Research AI Hardware Center, Center for Computational Innovation at Rensselaer Polytechnic Institute, and the U.S. Army Research Office.

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Learning to grow machine-learning models | MIT News | Massachusetts Institute of Technology - MIT News

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If you can't build a machine learning model from scratch or lack the infrastructure, merely connecting your app to a working model fixes the gap.

Artificial intelligence is here for everyone to use one way or the other. As for OpenAI Gym, there are many explorable training grounds to feed your reinforcement learning agents.

What is OpenAI Gym, how does it work, and what can you build using it?

OpenAI Gym is a Pythonic API that provides simulated training environments for reinforcement learning agents to act based on environmental observations; each action comes with a positive or negative reward, which accrues at each time step. While the agent aims to maximize rewards, it gets penalized for each unexpected decision.

The time step is a discrete-time tick for the environment to transit into another state. It adds up as the agent's actions change the environment state.

The OpenAI Gym environments are based on the Markov Decision Process (MDP), a dynamic decision-making model used in reinforcement learning. Thus, it follows that rewards only come when the environment changes state. And the events in the next state only depend on the present state, as MDP doesn't account for past events.

Before moving on, let's dive into an example for a quick understanding of OpenAI Gym's application in reinforcement learning.

Assuming you intend to train a car in a racing game, you can spin up a racetrack in OpenAI Gym. In reinforcement learning, if the vehicle turns right instead of left, it might get a negative reward of -1. The racetrack changes at each time step and might get more complicated in subsequent states.

Negative rewards or penalties aren't bad for an agent in reinforcement learning. In some cases, it encourages it to achieve its goal more quickly. Thus, the car learns about the track over time and masters its navigation using reward streaks.

For instance, we initiated the FrozenLake-v1 environment, where an agent gets penalized for falling into ice holes but rewarded for recovering a gift box.

Our first run generated fewer penalties with no rewards:

However, a third iteration produced a more complex environment. But the agent got a few rewards:

The outcome above doesn't imply that the agent will improve in the next iteration. While it may successfully avoid more holes the next time, it may get no reward. But modifying a few parameters might improve its learning speed.

The OpenAI Gym API revolves around the following components:

Since OpenAI Gym allows you to spin up custom learning environments, here are some ways to use it in a real-life scenario.

You can leverage OpenAI Gym's gaming environments to reward desired behaviors, create gaming rewards, and increase complexity per game level.

Where there's a limited amount of data, resources, and time, OpenAI Gym can be handy for developing an image recognition system. On a deeper level, you can scale it to build a face recognition system, which rewards an agent for identifying faces correctly.

OpenAI Gym also offers intuitive environment models for 3D and 2D simulations, where you can implement desired behaviors into robots. Roboschool is an example of scaled robot simulation software built using OpenAI Gym.

You can also build marketing solutions like ad servers, stock trading bots, sales prediction bots, product recommender systems, and many more using the OpenAI Gym. For instance, you can build a custom OpenAI Gym model that penalizes ads based on impression and click rate.

Some ways to apply OpenAI Gym in natural language processing are multiple-choice questions involving sentence completion or building a spam classifier. For example, you can train an agent to learn sentence variations to avoid bias while marking participants.

OpenAI Gym supports Python 3.7 and later versions. To set up an OpenAI Gym environment, you'll install gymnasium, the forked continuously supported gym version:

Next, spin up an environment. You can create a custom environment, though. But start by playing around with an existing one to master the OpenAI Gym concept.

The code below spins up the FrozenLake-v1. The env.reset method records the initial observation:

observation, info = env.reset()

Some environments require extra libraries to work. If you need to install another library, Python recommends it via the exception message.

For example, you'll install an additional library (gymnasium[toy-text]) to run the FrozenLake-v1 environment.

One of the setbacks to AI and machine learning development is the shortage of infrastructure and training datasets. But as you look to integrate machine learning models into your apps or devices, it's all easier now with ready-made AI models flying around the internet. While some of these tools are low-cost, others, including the OpenAI Gym, are free and open-source.

The rest is here:
What Is OpenAI Gym and How Can You Use It? - MUO - MakeUseOf