Interview with Michał Iwanowski, VP of Engineering, about AI development in 2021.
How do you rate 2020’s AI development?
AI is no longer a buzzword. Increasingly, from the R&D phase, it moves towards proven solutions, and nowhere more so than in computer vision and natural language processing.
The computer vision projects deepsense.ai delivered in 2020 prove that numerous industries can benefit from advanced image analysis technology. We have implemented successful projects in modern manufacturing that incorporated machine learning throughout the production process. These included not only intelligent quality control systems, but also safety monitoring and workplace security solutions. Our 2021 projects will develop this potential based on AIoT systems.
NLP-based solutions are likewise becoming more widely applicable. Our portfolio has been enriched by projects ranging from virtual meeting assistants to solutions capable of summarizing large chunks of text and evaluating the quality of student submissions in selected areas of study. With the rise of enormous language models such as GPT-3, we expect to see this area of AI – including natural language understanding (NLU) – grow more robustly in the coming quarters.
Where can we look for indicators of AI trends for 2021?
Top global research and consulting companies have indicated that 2021 is to be intensive and promising for AI. The emerging trends include the democratization of AI and industrialization of AI platforms. More and more businesses are seeing the real value in analyzing data and are convinced that using AI based on platforms that enable scalability and safety is a wise step forward.
The Covid-19 pandemic appears to have accelerated customers’ openness to AI. Despite the crisis, clients have continued to invest in the technology. They understand that in order to build competitive advantage, AI must become a core element of their business models. With more than 70 data science and machine learning experts on board, we are ready to turn clients’ vision into practical AI-focused solutions.
Do you notice any changes in your customers’ approach to AI solutions?
We work mainly with clients who already have some knowledge of and experience with AI projects. This allows us to act creatively on new AI solutions for various businesses. When a client retains us for a single project, we frequently uncover several new areas where we can further develop and implement AI.
We highly value clients who trust us enough to jointly take on challenges related to innovative AI applications. One such client is Volkswagen, with whom over the past 18 months we have moved neural network policy from a car simulator to reality. After multiple training sessions in the simulated environment, our algorithm overcame the sim-to-real gap in the real world. This milestone has opened up the possibility of using distributed computing to train AI models that are easily transferable to physical vehicles and can be tested in real world-environments.
We have also observed increased interest in our R&D projects. Many clients want to get involved in our research work because it leads them to explore new opportunities for their businesses. After a successful reinforcement learning project conducted together with Google Brain, the University of Warsaw and the University of Illinois at Urbana-Champaign, we continue our work in this area. The research conclusions bring tangible benefits to our clients by setting solid algorithmic foundations for tackling business problems that don’t present an obvious objective function, but rather require an AI agent to learn from its mistakes. This paradigm shift opens up brand new areas of application for AI.
Last but not least, we see many clients that are ready to build AI capabilities in-house. With our comprehensive training programs, they get best-in-class know-how that bears long-term results for real-life data challenges.
How do you sum up deepsense.ai’s plans for 2021 ?
Apart from pursuing new projects and challenges in computer vision, natural language processing and predictive analytics, there are two crucial tasks ahead of us in the upcoming year.
The first is to maintain an open-minded and creative approach to the changing reality of remote work and the migration of more and more services to the virtual environment – with a focus on how these transitions can be converted into business opportunities using AI and automation.
The second challenge is to bridge the gap between cutting-edge advancements on the frontier of AI and Machine Learning and reliable, well-tested business solutions. As a company that operates at the crossroads of business and research, we believe it’s on us to take a pioneering role in this challenge.
https://deepsense.ai/wp-content/uploads/2021/01/AI-trends-2021.jpg3371140deepsense.aihttps://deepsense.ai/wp-content/uploads/2019/04/DS_logo_color.svgdeepsense.ai2021-01-07 07:00:012021-01-07 08:52:48AI trends for 2021
Everything you need to know about demand forecasting – from the purpose and techniques to the goals and pitfalls to avoid.
Essential since the dawn of commerce and business, demand forecasting enters a new era of big-data rocket fuel.
What is demand forecasting?
The term couldn’t be clearer: demand forecasting forecasts demand. The process of predicting the future involves processing historical data to estimate the demand for a product. An accurate forecast can bring significant improvements to supply chain management, profit margins, cash flow and risk assessment.
What is the purpose of demand forecasting?
Demand forecasting is done to optimize processes, reduce costs and avoid losses caused by freezing up cash in stock or being unable to process orders due to being out of stock. In an ideal world, the company would be able to satisfy demand without overstocking.
Demand forecasting techniques
Demand forecasting is an essential component of every form of commerce, be it retail, wholesale, online, offline or multichannel. It has been present since the very dawn of civilization when intuition and experience were used to forecast demand.
Sybilla – deepsense.ai’s demand forecasting tool
More recent techniques combine intuition with historical data. Modern merchants can dig into their data in a search for trends and patterns. At the pinnacle of these techniques, are demand forecasting machine learning models, including gradient boosting and neural networks, which are currently the most popular ones and outperform classic statistics-based methods.
The basis of more recent demand forecasting techniques is historical data from transactions. These are data that sellers collect and store for fiscal and legal reasons. Because they are also searchable, these data are the easiest to use.
Sybilla – deepsense.ai’s demand forecasting tool
How to choose the right demand forecasting method – indicators
As always, selecting the right technique depends on various factors, including:
The scale of operations – the larger the scale, the more challenging processing the data becomes.
The organization’s readiness – even the large companies can operate (efficiency aside) on fragmented and messy databases, so the technological and organizational readiness to apply more sophisticated demand forecasting techniques is another challenge.
The product – it is easier to forecast demand for an existing product than for a newly introduced one. When considering the latter, it is crucial to forming a set of assumptions to work on. Owning as much information about the product as possible is the first step, as it allows the company to spot the similarities between particular goods and search for correlations in the buying patterns. Spotting an accessory that is frequently bought along with the main product is one example.
How demand forecasting can help a business
Demand forecasting and following sales forecasting is crucial to shaping a company’s logistics policy and preparing it for the immediate future. Among the main advantages of demand forecasting are:
Loss reduction – any demand that was not fulfilled should be considered a loss. Moreover, the company freezes its cash in stock, thus reducing liquidity.
Supply chain optimization – behind every shop there is an elaborate logistics chain that generates costs and needs to be managed. The bigger the organization, the more sophisticated and complicated its inventory management must be. When demand is forecast precisely, managing and estimating costs is easier.
Increased customer satisfaction – there is no bigger disappointment for consumers than going to the store to buy something only to return empty-handed. For a business, the worst-case scenario is for said consumers to swing over to the competition to make their purchase there. Companies reduce the risk of running out of stock–and losing customers–by making more accurate predictions.
Smarter workforce management – hiring temporary staff to support a demand peak is a smart way for a business to ensure it is delivering a proper level of service.
Better marketing and sales management – depending on the upcoming demand for particular goods, sales and marketing teams can shift their efforts to support cross- and upselling of complementary products,
Supporting expert knowledge – models can be designed to build predictions for every single product, regardless of how many there are. In small businesses, humans handle all predictions, but when the scale of the business and the number of goods rises, this becomes impossible. Machine learning models extend are proficient at big data processing.
How to start demand forecasting – a short guide
Building a demand forecasting tool or solution requires, first and foremost, data to be gathered.
While the data will eventually need to be organized, simply procuring it is a good first step. It is easier to structure and organize data and make them actionable than to collect enough data fast. The situation is much easier when the company employs an ERP or CRM system, or some other form of automation, in their daily work. Such systems can significantly ease the data gathering process and automate the structuring.
Sybilla – deepsense.ai’s demand forecasting tool
The next step is building testing scenarios that allow the company to test various approaches and their impact on business efficiency. The first solution is usually a simple one, and is a good benchmark for solutions to come. Every next iteration should be tested to see if it is performing better than the previous one.
Historical data is usually everything one needs to launch a demand forecasting project, and obviously, there are significantly less data on the future. But sometimes it is available, for example:
Short-term weather forecasts – the information about upcoming shifts in weather can be crucial in many businesses, including HoReCa and retail. It is quite intuitive to cross-sell sunglasses or ice cream on sunny days.
The calendar – Black Friday is a day like no other. The same goes for the upcoming holiday season or other events that are tied to a given date.
Sources of data that originate from outside the company make predictions even more accurate and provide better support for making business decisions.
Common pitfalls to avoid when building a demand forecasting solution
There are numerous pitfalls to avoid when building a demand forecasting solution. The most common of them include:
The data should be connected with the marketing and ads history – a successful promotion results in a significant change in data, so having information about why it was a success makes predictions more accurate. If machine learning was used to make the predictions, the model could have misattributed the changes and made false predictions based on wrong assumptions.
New products with no history – when new products are introduced, demand must still be estimated, but without the help of historical data. The good news here is that great strides have been made in this area, and techniques such as product DNA can help a company uncover similar products its past/current portfolio. Having data on similar products can boost the accuracy of prediction for new products.
The inability to predict the weather – weather drives demand in numerous contexts and product areas and can sometimes be even more important than the price of a product itself! (yes, classical economists would be very upset). The good news is that even if you are unable to predict the weather, you can still use it in your model to explain historical variations in demand.
Lacking information about changes – In an effort to support both short- and long-term goals, companies constantly change their offering and websites. When the information about changes is not annotated in the data, the model encounters sudden dwindles and shifts in demand with apparently no reason. In the reality, it is usually a minor issue like changing the inventory or removing a section from website.
Inconsistent portfolio information – predictions can be done only if the data set is consistent. If any of the goods in a portfolio have undergone a name or ID change, it must be noted in order not to confuse the system or miss out on a valuable insight.
Overfitting the model – a vicious problem in data science. A model is so good at working on the training dataset that it becomes inflexible and produces worse predictions when new data is delivered. Avoiding overfitting is down to the data scientists.
Inflexible logistics chain – the more flexible the logistics process is, the better and more accurate the predictions will be. Even the best demand forecasting model is useless when the company’s logistics is a fixed process that allows no space for changes.
Sybilla – deepsense.ai’s demand forecasting tool
Demand and sales forecasting is a crucial part of any business. Traditionally it has been done by experts, based on know-how honed through experience. With the power of machine learning it is now possible to combine the astonishing scale of big data with the precision and cunning of a machine-learning model. While the business community must remain aware of the multiple pitfalls it will face when employing machine learning to predict demand, there is no doubt that it will endow demand forecasting with awesome power and flexibility.
https://deepsense.ai/wp-content/uploads/2019/05/A-comprehensive-guide-to-demand-forecasting.jpg3371140Konrad Budekhttps://deepsense.ai/wp-content/uploads/2019/04/DS_logo_color.svgKonrad Budek2019-05-28 11:24:422021-01-05 16:45:37A comprehensive guide to demand forecasting
Although machine learning is seen as a monolith, this cutting-edge technology is diversified, with various sub-types including machine learning, deep learning, and the state-of-art technology of deep reinforcement learning.
What is reinforcement learning?
Reinforcement learning is the training of machine learning models to make a sequence of decisions. The agent learns to achieve a goal in an uncertain, potentially complex environment. In reinforcement learning, an artificial intelligence faces a game-like situation. The computer employs trial and error to come up with a solution to the problem. To get the machine to do what the programmer wants, the artificial intelligence gets either rewards or penalties for the actions it performs. Its goal is to maximize the total reward.
Although the designer sets the reward policy–that is, the rules of the game–he gives the model no hints or suggestions for how to solve the game. It’s up to the model to figure out how to perform the task to maximize the reward, starting from totally random trials and finishing with sophisticated tactics and superhuman skills. By leveraging the power of search and many trials, reinforcement learning is currently the most effective way to hint machine’s creativity. In contrast to human beings, artificial intelligence can gather experience from thousands of parallel gameplays if a reinforcement learning algorithm is run on a sufficiently powerful computer infrastructure.
Examples of reinforcement learning
Applications of reinforcement learning were in the past limited by weak computer infrastructure. However, as Gerard Tesauro’s backgamon AI superplayer developed in 1990’s shows, progress did happen. That early progress is now rapidly changing with powerful new computational technologies opening the way to completely new inspiring applications.
Training the models that control autonomous cars is an excellent example of a potential application of reinforcement learning. In an ideal situation, the computer should get no instructions on driving the car. The programmer would avoid hard-wiring anything connected with the task and allow the machine to learn from its own errors. In a perfect situation, the only hard-wired element would be the reward function.
For example, in usual circumstances we would require an autonomous vehicle to put safety first, minimize ride time, reduce pollution, offer passengers comfort and obey the rules of law. With an autonomous race car, on the other hand, we would emphasize speed much more than the driver’s comfort. The programmer cannot predict everything that could happen on the road. Instead of building lengthy “if-then” instructions, the programmer prepares the reinforcement learning agent to be capable of learning from the system of rewards and penalties. The agent (another name for reinforcement learning algorithms performing the task) gets rewards for reaching specific goals.
Another example: deepsense.ai took part in the “Learning to run” project, which aimed to train a virtual runner from scratch. The runner is an advanced and precise musculoskeletal model designed by the Stanford Neuromuscular Biomechanics Laboratory. Learning the agent how to run is a first step in building a new generation of prosthetic legs, ones that automatically recognize people’s walking patterns and tweak themselves to make moving easier and more effective. While it is possible and has been done in Stanford’s labs, hard-wiring all the commands and predicting all possible patterns of walking requires a lot of work from highly skilled programmers.
For more real-life applications of reinforcement learning check this article.
The main challenge in reinforcement learning lays in preparing the simulation environment, which is highly dependant on the task to be performed. When the model has to go superhuman in Chess, Go or Atari games, preparing the simulation environment is relatively simple. When it comes to building a model capable of driving an autonomous car, building a realistic simulator is crucial before letting the car ride on the street. The model has to figure out how to brake or avoid a collision in a safe environment, where sacrificing even a thousand cars comes at a minimal cost. Transferring the model out of the training environment and into to the real world is where things get tricky.
Scaling and tweaking the neural network controlling the agent is another challenge. There is no way to communicate with the network other than through the system of rewards and penalties.This in particular may lead to catastrophic forgetting, where acquiring new knowledge causes some of the old to be erased from the network (to read up on this issue, see this paper, published during the International Conference on Machine Learning).
Yet another challenge is reaching a local optimum – that is the agent performs the task as it is, but not in the optimal or required way. A “jumper” jumping like a kangaroo instead of doing the thing that was expected of it-walking-is a great example, and is also one that can be found in our recent blog post.
Finally, there are agents that will optimize the prize without performing the task it was designed for. An interesting example can be found in the OpenAI video below, where the agent learned to gain rewards, but not to complete the race.
What distinguishes reinforcement learning from deep learning and machine learning?
In fact, there should be no clear divide between machine learning, deep learning and reinforcement learning. It is like a parallelogram – rectangle – square relation, where machine learning is the broadest category and the deep reinforcement learning the most narrow one.
In the same way, reinforcement learning is a specialized application of machine and deep learning techniques, designed to solve problems in a particular way.
Although the ideas seem to differ, there is no sharp divide between these subtypes. Moreover, they merge within projects, as the models are designed not to stick to a “pure type” but to perform the task in the most effective way possible. So “what precisely distinguishes machine learning, deep learning and reinforcement learning” is actually a tricky question to answer.
Machine learning – is a form of AI in which computers are given the ability to progressively improve the performance of a specific task with data, without being directly programmed ( this is Arthur Lee Samuel’s definition. He coined the term “machine learning”, of which there are two types, supervised and unsupervised machine learning
Supervised machine learning happens when a programmer can provide a label for every training input into the machine learning system.
Example – by analyzing the historical data taken from coal mines, deepsense.ai prepared an automated system for predicting dangerous seismic events up to 8 hours before they occur. The records of seismic events were taken from 24 coal mines that had collected data for several months. The model was able to recognize the likelihood of an explosion by analyzing the readings from the previous 24 hours.
Some of the mines can be exactly identified by their main working height values. To obstruct the identification, we added some Gaussian noise
From the AI point of view, a single model was performing a single task on a clarified and normalized dataset. To get more details on the story, read our blog post. Unsupervised learning takes place when the model is provided only with the input data, but no explicit labels. It has to dig through the data and find the hidden structure or relationships within. The designer might not know what the structure is or what the machine learning model is going to find.
An example we employed was for churn prediction. We analyzed customer data and designed an algorithm to group similar customers. However, we didn’t choose the groups ourselves. Later on, we could identify high-risk groups (those with a high churn rate) and our client knew which customers they should approach first.
Another example of unsupervised learning is anomaly detection, where the algorithm has to spot the element that doesn’t fit in with the group. It may be a flawed product, potentially fraudulent transaction or any other event associated with breaking the norm.
Deep learning consists of several layers of neural networks, designed to perform more sophisticated tasks. The construction of deep learning models was inspired by the design of the human brain, but simplified. Deep learning models consist of a few neural network layers which are in principle responsible for gradually learning more abstract features about particular data.
Although deep learning solutions are able to provide marvelous results, in terms of scale they are no match for the human brain. Each layer uses the outcome of a previous one as an input and the whole network is trained as a single whole. The core concept of creating an artificial neural network is not new, but only recently has modern hardware provided enough computational power to effectively train such networks by exposing a sufficient number of examples. Extended adoption has brought about frameworks like TensorFlow, Keras and PyTorch, all of which have made building machine learning models much more convenient.
Example: deepsense.ai designed a deep learning-based model for the National Oceanic and Atmospheric Administration (NOAA). It was designed to recognize Right whales from aerial photos taken by researchers. For further information about this endangered species and deepsense.ai’s work with the NOAA, read our blog post. From a technical point of view, recognizing a particular specimen of whales from aerial photos is pure deep learning. The solution consists of a few machine learning models performing separate tasks. The first one was in charge of finding the head of the whale in the photograph while the second normalized the photo by cutting and turning it, which ultimately provided a unified view (a passport photo) of a single whale.
The third model was responsible for recognizing particular whales from photos that had been prepared and processed earlier. A network composed of 5 million neurons located the blowhead bonnet-tip. Over 941,000 neurons looked for the head and more than 3 million neurons were used to classify the particular whale. That’s over 9 million neurons performing the task, which may seem like a lot, but pales in comparison to the more than 100 billion neurons at work in the human brain. We later used a similar deep learning-based solution to diagnose diabetic retinopathy using images of patients’ retinas. Reinforcement learning, as stated above employs a system of rewards and penalties to compel the computer to solve a problem by itself. Human involvement is limited to changing the environment and tweaking the system of rewards and penalties. As the computer maximizes the reward, it is prone to seeking unexpected ways of doing it. Human involvement is focused on preventing it from exploiting the system and motivating the machine to perform the task in the way expected. Reinforcement learning is useful when there is no “proper way” to perform a task, yet there are rules the model has to follow to perform its duties correctly. Take the road code, for example.
In particular, if artificial intelligence is going to drive a car, learning to play some Atari classics can be considered a meaningful intermediate milestone. A potential application of reinforcement learning in autonomous vehicles is the following interesting case. A developer is unable to predict all future road situations, so letting the model train itself with a system of penalties and rewards in a varied environment is possibly the most effective way for the AI to broaden the experience it both has and collects.
The key distinguishing factor of reinforcement learning is how the agent is trained. Instead of inspecting the data provided, the model interacts with the environment, seeking ways to maximize the reward. In the case of deep reinforcement learning, a neural network is in charge of storing the experiences and thus improves the way the task is performed.
Is reinforcement learning the future of machine learning?
Although reinforcement learning, deep learning, and machine learning are interconnected no one of them in particular is going to replace the others. Yann LeCun, the renowned French scientist and head of research at Facebook, jokes that reinforcement learning is the cherry on a great AI cake with machine learning the cake itself and deep learning the icing. Without the previous iterations, the cherry would top nothing.
In many use cases, using classical machine learning methods will suffice. Purely algorithmic methods not involving machine learning tend to be useful in business data processing or managing databases.
Sometimes machine learning is only supporting a process being performed in another way, for example by seeking a way to optimize speed or efficiency.
When a machine has to deal with unstructured and unsorted data, or with various types of data, neural networks can be very useful. How machine learning improved the quality of machine translation has been described by The New York Times.
Reinforcement learning is no doubt a cutting-edge technology that has the potential to transform our world. However, it need not be used in every case. Nevertheless, reinforcement learning seems to be the most likely way to make a machine creative – as seeking new, innovative ways to perform its tasks is in fact creativity. This is already happening: DeepMind’s now famous AlphaGo played moves that were first considered glitches by human experts, but in fact secured victory against one of the strongest human players, Lee Sedol.
Thus, reinforcement learning has the potential to be a groundbreaking technology and the next step in AI development.
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