Creation of the Mythical AI — Bringing Artificial Intelligence Reporting Back to Earth
Artificial intelligence is a key enabling technology for connected autonomous vehicles (self driving cars in particular) and a host of…
Category: Artificial Intelligence
Author: Adam Mackay

Creation of the Mythical AI — Bringing Artificial Intelligence Reporting Back to Earth
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Artificial intelligence is a key enabling technology for connected autonomous vehicles (self driving cars in particular) and a host of other important technological advances. But are we becoming too tied up in the reporting hype?
Since 2015, the wave of sensational artificial intelligence stories is in full swing. AI is a regular political debating topic. Exciting announcements bombard us and professional forecasters portray a future sometimes utopian and most often dystopian, focusing on extremes like a well-written science fiction story.
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The media routinely disguises and exaggerates reality. Abilities are bestowed on AI it which it does not possess and is not ready to have. We are building a mythical vision of AI, based on propaganda and early innovation. The marketing communications of suppliers, such as IBM and Google, amplify and embellish progress through a strategy of amazement. Checking the intricate detail of reports is beyond the casual observer and hampered by the widespread absence of technical knowledge on AI, even among of digital specialists.
This term AI was first used in 1955 by John MacCarthy when he proposed holding of a summer camp at Dartmouth College in New Hampshire. It was a marketing phrase created to talk about a dispersed collection of technologies rather than an emerging field of study. Today the debates are endless as scientists, philosophers and the media struggle to define the nature of human intelligence and how much of it machines can gain. Sometimes it is easier to refer to AI in terms of what it cannot yet do. For many revealing the magic of today’s AI destroys their established definition of AI.
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According to current articles… IBM Watson is the swiss army knife AI who knows how to do everything, DeepMind AlphaGo is more intelligent than the best strategy game players, the latest Intel processor Loihi mimics the brain, autonomous cars are happening now and miracle marketing solutions predict in advance what you want. The AI peddled by the media embodies a future which will remove all chance and free will in our little monotonous lives.
Jobs will disappear, a valid concern for many. Forecasts are predicting up to 50% by 2025. However, reports fail to mention that much of elimination of trades does not come from AI but general automation and transferring work to customers, for example, automatic cash registers, or by distributing work through the emerging collaborative economy.
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Journalists effective in their communication are rarely specialists in AI in the scientific sense of the term. And when they are, subtle exaggerations accumulate. An example is that of singularity advocate Ray Kurzweil who offers up quotes regularly. From what you read you would think he is directing all of Google’s AI research. In reality, he is only a small part of Google’s AI effort, the two main divisions are Google Brain and Deep Mind.
By thinking only of the future, we forget the present. Obsession with AI creates a delirious atmosphere that becomes disruptive to capitalism. Talk of AI will interest companies, and scare them. But chiefly, AI wastes management time as they wonder how to integrate the shiny new thing into their strategy. Many conventional businesses need to return to Earth, to deliver practical and workable things.
To help you navigate the sensationalism that accompanies AI, here are a selection of myths surrounding AI that grate with me.
- AI algorithms have not improved in the last 20 years
Many reports suggest AI algorithms have not evolved. Better hardware and the abundance of exploitable data are the reason for recent advances. This is not true.
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Algorithmic progress is slow in machine learning because it relies on classification, prediction and segmentation methods whos roots are one to two decades old. It is different in deep learning. Deep learning dates back to 2006 and its techniques have been evolving since. In 2016 alone, the convolutional neural networks used for image recognition have undergone many iterations, also the generative networks capable of transforming and generating images. Only since 2015 have these systems had the ability to tag multiple objects in an picture or video.
The family of neural network types is complex. Deep learning requires assembly of these bricks that constantly evolve. The general aim of AI development comprises two complementary objectives: to improve the qualitative performance of neural networks and to speed up training. The latter is very expensive in terms of machine resources, even with GPUs and neuromorphic processors such as Googles TPU.
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Memory neural networks used for language processing (recognition, translation, generation) have many variations, including: Stacked RNN (RNN stacked), MARNN (Memory-Augmented Recurrent Neural Networks), LSTM (Long Short-Term Memory), GRU (Gated Recurrent Units) BLSTM (Bidirectional Long Short-Term Memory), BPTT (BackProp Through Time) and RTRL (Real Time Recurrent Learning). They take into account the context in which the objects like words are detected to analyze the meaning of a sentence. One of the key points of these networks is their ability to memorize contexts.
The pleasant quirk of AI research is that many advances are exploitable rather quickly because they are public and reproducible with the open source tools of the market such as TensorFlow or Torch.
- AI only comprises machine learning and deep learning
Deep learning has come to the fore as the miracle solution to address all problems. Deep learning is so called because its neural networks are a multilayer. This does not make them intelligent. Many papers have shown that human intelligence does not work like the current deep learning neural networks. These only a statistical and numerical view of the objects they analyze. It is neither semantic nor analytic. Deep learning is not the ultimate outcome of AI, it only serves to solve certain classes of problems.
The broad field of AI includes other techniques, including symbolic AI, logic programming, and rule engines. News has muted them because of the glamour of deep learning. The best AI solutions often integrate and assemble several approaches.
- Useful data is the main component of AI
Data is one of the three key components of AI solutions, together with the computational grunt and algorithms. Without all these elements, you don’t have AI.
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Effective algorithms play a key role in the quality of the results in deep learning, and in their performance, particularly the speed of the training phase of the models. Sometimes it may also be necessary to combine many methods to produce useful output.
To determine performance in image recognition, training time is compared to the percentage of successful identification. The progress of the algorithms aims to improve one or the other. The size of the datasets is however critical to train a model. If the algorithm used is better than the training speed, which is often the case in convolutional neural network variants, then the recognition performance will not change when the trained model is run… with more training data, it will be longer. You need both better data sets and better algorithms so that the training is as fast as possible and the results generate minimal errors.
- Training an AI happens quickly
The training time of a single-function deep learning system takes hours or days, or even weeks for image recognition for medical imaging interpretation. What’s often overlooked is that the AI does not train alone. It requires a lot of iterations with human intervention.
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The ideal of self-learning systems has not yet been achieved. Data scientists must do many tests and pay close supervision to select the right statistical models of segmentation, classification or prediction. For deep learning neural networks, researchers must perform tests to determine the major characteristics and evaluate their effectiveness with the available datasets. Iterations can be long and laborious. They can last several days, which is much longer than the compilation of code or the immediate nature of the interpreted software written today.
Once a machine learning model has been successfully trained, its operational execution is then quick. With deep learning, this is simple to understand: a convolutional image recognition neural network can include millions of parameters that must be adjusted by testing for each object in the test database. This generates a huge computational overhead. Once the model is trained, it suffices to load the static variables determined in the training phase and to pass the object to be recognized through the network of neurons. And since it is software, the cost of replication and execution of the solution is low.
In simple terms, AI gives us laborious training and an inexpensive operation. The neuromorphic cores in the latest mobile processors (Kirin 970 for Huawei and A11 Bionic Neural Engine for the iPhone 8 and X) are not powerful (1 to 2 TFlops/s) but they are only used for execution of neural networks already trained on servers with much more power. On the device, pre-built models are used for the recognition of images or speech.
The media also overlook that most AI solutions, such as those of medical imaging, rely on knowledge that is the result of lengthy scientific research carried out by thousands of specialists. When AlphaGo wins against the world champion, it does so by analyzing 150,000 games played by the best players in the world. This human expertise took a long time to build. The AI is a “free rider” which exploits this long accumulation of data.
Maybe one-day RNNs will interact with the physical world and accumulate high-level knowledge based on experimentation. Today, this principle is being explored using physical robots that learn to move by trial and error. The intellectual equivalent is not there yet, but researchers are working on it.
- AI is ‘free’
AI only appears free in consumer online services funded by advertising. Our perception of ‘free’ is always the case in business models where the user is not the buyer. This illusion is not specific to AI.
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The emergence of AI is big business for the cloud and the data centers it requires for its operation. As an example, a single Nvidia DGX1 server costs an eye-watering $129K. The hardware costs will fall helped by the effect of Moores Law. However, as the hardware cost fall, the sophistication of AI applications will increase demanding more power. For the next few years at least the pace of hardware improvement will not catch up with the computing power demanded by AI.
In situations where software or an AI can replace human activity, there are cost savings, particularly in developed countries where wages are high. AI does not have to be free to compete.
- AI will exceed exponential growth leading to a ‘Singularity’ event
Progress AI performance is significant, however, the rate of improvements are unlikely to be sustained in the long term. Between the last two generations of Nvidia GPU (Pascal V100 2016 and Volta GV100 2017), the gross performance gain in the calculations related to deep learning range from a factor 1.8 to 9.3. This is related to the use of matrix multipliers in this new generation of GPUs, a technique that was established recently and incorporated into the newest neuromorphic processors.
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In practice, the training of a ResNet convolutional neural network for image recognition is 2.4 times faster and its execution in run-time is 3.7 times faster between these two generations of GPUs. This is great but we are far from exponential. It is, however, possible to make use of parallelization… if you line up 40 processors the training will be 100 times faster than with an older generation GPU since the deep learning frameworks know how to parallelize the training on several GPUs.
Exponential growth could, however, be achieved using as yet unproven forms of computational devices. Future optical and quantum processors will each be able to boost AI progress significantly in the execution of AI models and especially training.
-Artificial General Intelligence (AGI) will emerge from deep learning techniques
One flaw of this theorem is that it relies on a false premise that intelligence will emerge from increased computing power alone. The computational capabilities of the human brain (which calculates little and is massively parallel) are that not that similar to current AI machines (which do binary operations on matrices).
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AGI is the mechanical outcome of the blind application of Moore’s Law. As more discoveries are made on the complexity of the brain, then the predicted near future emergence of AGI appears less and less likely. We still do not know how to describe how the brain works in terms of memory or computation. Neurons are much more nuanced than estimated 10 or 15 years ago.
Human intelligence is complex. It integrates the sophistication of the brain to our senses, our experiences and our perception of the world. Biological senses are even more difficult to imitate by machines than intellectual abilities. Reports always talk about the cortex that manages the intelligence but neglect the cerebellum, this part of the ultra-dense brain that includes four times more neurons than the cortex and manages automated learned tasks (walking, eye movements, driving, cycling, walking, dance, recognition, …).
AI futurists generally adopt a too linear view of the history of technology. In reality, progress follows several paths in parallel. Experience shows that machines paired with humans are superior to machines alone. Humans and machines do not have the same capabilities and complement each other. The machines already far exceed us in a lot of applications without it needing superhuman intelligence.
There is no doubt that AI will drive a major evolution of digital tools. But we must understand the issues and opportunities. These are not sensational. The potential of AI is huge for companies in all sectors of activity but still, you need to keep your head on your shoulders to apprehend it calmly. Today’s technologies are already rich enough to be creative and innovative. Over-automation has limitations that need to be tested. Companies must keep humans in the loop and not spend their time hiding behind software.