How MNC’s leverage the power of Neural Networks
Today, Neural Networks have brought a next-level revolution in the field of Artificial Intelligence.
No one could have imagined what would happen when machines will be given the same intelligence and the way of operating like that of a human mind. And one can clearly see, it has ended so well that now machines are trained to learn, recognize patterns, and make predictions in a humanoid fashion as well as solve problems in every business sector.
Neural Networks
Neural networks are mathematical models that use learning algorithms inspired by the brain to store information. Since neural networks are used in machines, they are collectively called an ‘artificial neural network.’ Nowadays, the term machine learning is often used in this field and is the scientific discipline that is concerned with the design and development of algorithms that allow computers to learn, based on data, such as from sensor data or databases. A major focus of machine-learning research is to automatically learn to recognize complex patterns and make intelligent decisions based on data. Hence, machine learning is closely related to fields such as statistics, data mining, pattern recognition, and artificial intelligence. Neural networks are a popular framework to perform machine learning, but there are many other machine-learning methods, such as logistic regression, and support vector machines.
What are neural networks?
Neural networks, also known as artificial neural networks (ANNs) or simulated neural networks (SNNs), are a subset of machine learning and are at the heart of deep learning algorithms. Their name and structure are inspired by the human brain, mimicking the way that biological neurons signal to one another.
Artificial neural networks (ANNs) are comprised of node layers, containing an input layer, one or more hidden layers, and an output layer. Each node, or artificial neuron, connects to another and has an associated weight and threshold. If the output of any individual node is above the specified threshold value, that node is activated, sending data to the next layer of the network. Otherwise, no data is passed along to the next layer of the network.
Neural networks rely on training data to learn and improve their accuracy over time. However, once these learning algorithms are fine-tuned for accuracy, they are powerful tools in computer science and artificial intelligence, allowing us to classify and cluster data at a high velocity. Tasks in speech recognition or image recognition can take minutes versus hours when compared to the manual identification by human experts. One of the most well-known neural networks is Google’s search algorithm.
How do neural networks work?
Think of each node as its own linear regression model, composed of input data, weights, a bias (or threshold), and an output. The formula would look something like this:
Once an input layer is determined, weights are assigned. These weights help determine the importance of any given variable, with larger ones contributing more significantly to the output compared to other inputs. All inputs are then multiplied by their respective weights and then summed. Afterward, the output is passed through an activation function, which determines the output. If that output exceeds a given threshold, it “fires” (or activates) the node, passing data to the next layer in the network. This results in the output of one node becoming in the input of the next node. This process of passing data from one layer to the next layer defines this neural network as a feedforward network.
Let’s break down what one single node might look like using binary values. We can apply this concept to a more tangible example, like whether you should go surfing (Yes: 1, No: 0). The decision to go or not to go is our predicted outcome or y-hat. Let’s assume that three factors are influencing your decision-making:
- Are the waves good? (Yes: 1, No: 0)
- Is the line-up empty? (Yes: 1, No: 0)
- Has there been a recent shark attack? (Yes: 0, No: 1)
Then, let’s assume the following, giving us the following inputs:
- X1 = 1, since the waves are pumping
- X2 = 0, since the crowds are out
- X3 = 1, since there hasn’t been a recent shark attack
Now, we need to assign some weights to determine importance. Larger weights signify that particular variables are of greater importance to the decision or outcome.
- W1 = 5, since large swells don’t come around often
- W2 = 2, since you’re used to the crowds
- W3 = 4, since you have a fear of sharks
Finally, we’ll also assume a threshold value of 3, which would translate to a bias value of –3. With all the various inputs, we can start to plug values into the formula to get the desired output.
Y-hat = (1*5) + (0*2) + (1*4) — 3 = 6
If we use the activation function from the beginning of this section, we can determine that the output of this node would be 1, since 6 is greater than 0. In this instance, you would go surfing; but if we adjust the weights or the threshold, we can achieve different outcomes from the model. When we observe one decision, like in the above example, we can see how a neural network could make increasingly complex decisions depending on the output of previous decisions or layers.
In the example above, we used perceptrons to illustrate some of the mathematics at play here, but neural networks leverage sigmoid neurons, which are distinguished by having values between 0 and 1. Since neural networks behave similarly to decision trees, cascading data from one node to another, having x values between 0 and 1 will reduce the impact of any given change of a single variable on the output of any given node, and subsequently, the output of the neural network.
As we start to think about more practical use cases for neural networks, like image recognition or classification, we’ll leverage supervised learning, or labeled datasets, to train the algorithm. As we train the model, we’ll want to evaluate its accuracy using a cost (or loss) function. This is also commonly referred to as the mean squared error (MSE). In the equation below,
- I represent the index of the sample,
- y-hat is the predicted outcome,
- y is the actual value, and
- m is the number of samples.
Ultimately, the goal is to minimize our cost function to ensure the correctness of fit for any given observation. As the model adjusts its weights and bias, it uses the cost function and reinforcement learning to reach the point of convergence or the local minimum. The process in which the algorithm adjusts its weights is through gradient descent, allowing the model to determine the direction to take to reduce errors (or minimize the cost function). With each training example, the parameters of the model adjust to gradually converge at the minimum.
Most deep neural networks are feedforward, meaning they flow in one direction only, from input to output. However, you can also train your model through backpropagation; that is, move in the opposite direction from output to input. Backpropagation allows us to calculate and attribute the error associated with each neuron, allowing us to adjust and fit the parameters of the model(s) appropriately.
Neural networks are broadly used, with applications for financial operations, enterprise planning, trading, business analytics, and product maintenance. Neural networks have also gained widespread adoption in business applications such as forecasting and marketing research solutions, fraud detection, and risk assessment.
A neural network evaluates price data and unearths opportunities for making trade decisions based on the data analysis. The networks can distinguish subtle nonlinear interdependencies and patterns other methods of technical analysis cannot. According to research, the accuracy of neural networks in making price predictions for stocks differs. Some models predict the correct stock prices 50 to 60 percent of the time while others are accurate in 70 percent of all instances. Some have posited that a 10 percent improvement in efficiency is all an investor can ask for from a neural network.1
There will always be data sets and task classes that a better analyzed by using previously developed algorithms. It is not so much the algorithm that matters; it is the well-prepared input data on the targeted indicator that ultimately determines the level of success of a neural network.
How brains differ from computers
You often hear people comparing the human brain and the electronic computer and, on the face of it, they do have things in common. A typical brain contains something like 100 billion minuscule cells called neurons (no-one knows exactly how many there are and estimates go from about 50 billion to as many as 500 billion).[1] Each neuron is made up of a cell body (the central mass of the cell) with several connections coming off it: numerous dendrites (the cell’s inputs — carrying information toward the cell body) and a single axon (the cell’s output — carrying information away). Neurons are so tiny that you could pack about 100 of their cell bodies into a single millimeter. (It’s also worth noting, briefly in passing, that neurons make up only 10–50 percent of all the cells in the brain; the rest are glial cells, also called neuroglia, that support and protect the neurons and feed them with energy that allows them to work and grow.)Inside a computer, the equivalent to a brain cell is a nanoscopically tiny switching device called a transistor. The latest, cutting-edge microprocessors (single-chip computers) contain over 30 billion transistors; even a basic Pentium microprocessor from about 20 years ago had about 50 million transistors, all packed onto an integrated circuit just 25mm square (smaller than a postage stamp)!
Artwork: A neuron: the basic structure of a brain cell, showing the central cell body, the dendrites (leading into the cell body), and the axon (leading away from it).
That’s where the comparison between computers and brains begins and ends because the two things are completely different. It’s not just that computers are cold metal boxes stuffed full of binary numbers, while brains are warm, living, things packed with thoughts, feelings, and memories. The real difference is that computers and brains “think” in completely different ways. The transistors in a computer are wired in relatively simple, serial chains (each one is connected to maybe two or three others in basic arrangements known as logic gates), whereas the neurons in a brain are densely interconnected in complex, parallel ways (each one is connected to perhaps 10,000 of its neighbors).
Top MNCs Using Neural Networks
Here are the top MNCs that are using Neural Networks and Machine Learning at its best:
Google is known to be the greatest and the most advanced company when it comes to the field of AI and Machine Learning.
The main reason for this is probably the amount of money the company has spent acquiring startups — Google has spent more than any other, according to CB Insights.
Google is taking complete advantage of Neural Network, deep learning and AI. Google had launched multiple AI chatbots that answer messages for you — like a more sophisticated auto-response email — in a range of contexts, including Skype, Slack and Twitter direct messages.
But Google’s strongest point in this area is probably the range of cloud-based services it offers developers, including the Google Cloud AI machine learning tools.
2. Baidu
Another company which has been very active in the mergers and acquisitions scene is Chinese search giant Baidu.
The company is particularly interested in natural language processing, with a view to developing a workable voice-activated search function.
One of the many machine learning acquisitions Baidu made was Kitt.ai, which is said to have a portfolio of chatbots and voice-based applications. The financial terms of that particular deal were not disclosed but Baidu is said to be the 10th biggest-spender on acquisitions in the world, according to CB Insights, which says Google spends the most.
3. Apple
There might be a perception that Apple is late to the machine learning party, but that’s probably not true, especially since it was the first to launch a voice assistant on a smartphone.
Millions of people talk to Siri, even if we don’t, and Apple is looking to extend the application of the talking assistant through its new smart home device or speaker, the HomePod.
Apple has also been active in acquisitions — second only to Google.
One of the more notable purchases has been Lattice Data, which has a machine learning system for converting unstructured data — like random text and pictures — into structured data.
4. Microsoft
Microsoft was actually the third-biggest spender on acquisitions over the past few years, according to CB Insights.
The company is well and truly into the internet market, especially after its $26 billion purchase of LinkedIn a couple of years ago.
But probably the most significant acquisition Microsoft made in the machine learning space was Maluuba, which the tech giant says has “one of the world’s most impressive deep learning research labs for natural language understanding”.
