A Beginner’s Guide to Artificial Neural Networks – Part 3August 31, 2020 - 7 minutes read
Welcome to the third part of our special series on artificial neural networks! These computing systems have spurred numerous innovations in artificial intelligence (AI) development over the last decade. In our previous post, we explored how artificial neural networks function and learn. Did you miss it? Check it out here.
For this final entry of our series, we’ll examine some of the most relevant artificial neural network applications in our world today. Let’s get started!
1. Conversational Chatbots
Only a few years ago, chatboxes picked up big momentum across the Internet. First-time website visitors would be greeted by a human agent on the other end of a chatbox who could help you out with a variety of queries. And then natural language processing (NLP) came along and changed everything with the introduction of conversational chatbots.
NLP is a subfield of computer science, linguistics, and AI that leverages statistical rules to replicate natural human language interactions. Like machine learning applications and artificial neural networks, NLP algorithms get better with more training.
Speaking of which, everything from essays and news articles to restaurant reviews and song lyrics can be used to train NLP algorithms — the right material really depends on your goals. With the right training input, conversational chatbots can understand the context of a user’s queries and formulate an appropriate, helpful response.
2. Personalized Recommendations
Perhaps one of the pertinent artificial neural network applications is the ability to make personalized recommendations for every user. You’ve probably encountered this while shopping on the website of Seattle-based tech titan Amazon or browsing through the plethora of entertainment options on Netflix’s streaming service.
To make personalized recommendations work, artificial neural networks need a wealth of data. In terms of e-commerce, previous user purchases, geographic information, demographic data, and even what people who bought the same product are going to buy next are all helpful. The more input, the better the artificial neural network can be at recommending suitable options for you.
Unsurprisingly, what you do end up buying helps to optimize the algorithm. Essentially, every purchase you make on an e-commerce platform like Amazon enriches the artificial neural network’s capabilities.
3. Personal Loans
Traditionally, financial institutions would use actuarial tables to determine the risk factor associated with an insurance or loan applicant. But today, artificial neural networks have taken this method to a higher level.
After inputting your information and establishing the correct weights to use, lenders can rapidly review the decades of data they have on-hand to determine your loan application’s risk profile. As far as personal data goes, factors such as your age, gender, alma mater, industry of work, salary, city of residence, and savings ratio can all be considered to determine your credit risk score.
Previously, your individual credit score would be the sole or major determinant of whether you were approved for a loan or not. Fortunately, artificial neural networks have made the process much more comprehensive. Consequently, several FinTech startups have entered the personal loans arena to evaluate and lend to people who are deemed too risky by traditional institutions.
4. Autonomous Driving
The biggest names in the self-driving space, such as Tesla, Uber, and Waymo, all use artificial neural networks to make this science fiction concept a reality. Autonomous vehicles have to process gargantuan amounts of information in real-time. This can come in the form of road signs, traffic lights, nearby vehicles, pedestrians, and so on.
It’s worth noting that the artificial neural networks behind these self-driving vehicles are much more advanced and complex than the ones we’ve discussed in this series. However, they do operate on many of the same principles that we’ve examined.
5. High-Profile Event Outcome Prediction
Whether it’s political processes like presidential elections or big sporting events like the FIFA World Cup, artificial neural networks are now being used to predict the outcomes. Both of these examples are phased — there are discrete moments in which candidates or teams are eliminated. This helps the algorithms behind artificial neural networks to understand their efficacy.
Still, there is an immense amount of challenge in properly capturing all of the input variables involved in these situations. For instance, to predict the FIFA World Cup winner, an artificial neural network must have access to player stats, demographics, anatomical capabilities, team records, and much more. This issue is magnified even more when it comes to the stock market.
Predictive algorithms that employ artificial neural networks to predict stock market action have been around for quite some time now. Usually, they use financial metrics and news updates as input variables. With these insights in tow, exchanges and banks conduct high-frequency trading of assets at speeds that could never possibly be matched by humans.
But in stock markets, the data always contains noise. Due to the degree of subjective judgment involved, randomness is rampant, and this can substantially impact the price of a security. Regardless, every leading financial institution is still tapping into the calculative prowess of artificial neural networks to make big moves in the market.
Artificial Neural Networks Are Everywhere
We hope you’ve enjoyed this overview of how artificial neural networks are being used in society today! The truth is that this list only scratches the surface of what this computing technology can do. Artificial neural networks are also helping with mental health diagnoses, medical imaging, drone deliveries, and several other applications every day.
As they become more sophisticated, we’ll start to see artificial neural networks crop up in other industries and sectors. What use of artificial neural networks makes you the most excited? Can you think of any other ways that they’re not being used, but should be? As always, let us know your thoughts in the comments below!Tags: AI and machine learning, AI and ML, AI app developer Seattle, AI app developers Seattle, AI app development Seattle, AI vs ML, app developers Seattle, artificial intelligence app development, machine learning app development, machine learning apps, machine learning developers Seattle, machine learning use cases, machine learning uses, ML and AI, ML app developer, mobile app developer Seattle, mobile app development Seattle