If you have ever heard of or read about AI, then you have probably noticed that most AIs in the media are for entertainment purposes. They are designed to trick humans into laughing at funny jokes, acting out hilarious scenarios, and watching interesting videos.
This is not how most AIs exist in real life though. While there are some very advanced AIs used for things such as games, they are still far away from having their own independent company, let alone making enough money to pay someone else to do their job.
Deep learning systems are what make up the majority of AI today. Systems like these use large amounts of data, computational power, and neural networks (NNs) to teach themselves new tasks.
These NNs can be connected together in really complex ways to perform all sorts of different functions. Companies hire computers to train DL algorithms so that they can add more connections and layers to improve performance on specific tasks.
The best example of this is Google’s famous AI system, called DeepMind. Over the past few years, it has trained itself to play computer games extremely well!
Google paid DeepMind a whopping $400 million to start owning one of its projects back in 2016. Since then, it has continued investing heavily in the AI research and development team within it.
And while gamers might think that this investment is only focused on bettering the game engine, it actually does something much bigger.
Applies AI to their search engine
Since the launch of Google Search in 1998, it’s incorporated artificial intelligence (AI) into its algorithm to make better decisions for users. This is how they achieve lightning-fast page loads and efficient keyword research. By using natural language processing (NLP), which analyzes large amounts of text data, and machine learning (ML), which teaches algorithms to process this information, Google has refined its search experience to focus more on giving you the best results possible rather than just showing you what they think you want to see.
In fact, back when Larry Page and Sergey Brin were developing the platform, they referred to it as “assistive technology.” They meant it to help people with limited digital literacy find relevant content, but these days it does much more.
Deepfakes and other types of fake media have proven particularly damaging because they can spread false or misleading information at an alarming rate. Even if most people are able to tell whether something seems off, there’s still a risk that some won’t.
Advertisements are targeted
Whilst most of us use advertisements to inform, influence or motivate us to purchase a product or service, advertisers go one step further – they target their ads towards individuals or groups with specific traits.
By using this tactic, advertisers can create hype around a product before finally asking you to buy it. It’s like buying a car without test driving it first!
By analyzing your online activity, smartphone data and other sources, advertising companies identify patterns that indicate who is likely to want what products and services.
This information is used to match you with appropriate advertisements, videos and websites that feature the item you’re looking for. If you’ve ever noticed spammy emails from fake credit card providers, those were inspired by people in your email list.
Advertising isn’t just about getting you to spend money, it’s also about maximizing profits for marketing firms. By targeting certain types of customers, they’ll invest more money in commercials, which will generate revenue for them. You get paid for being influenced, not for doing something directly.
Users pay for the service
Recent developments in AI have sparked an explosion of interest in what are called “deep learning” systems. These sophisticated computer programs can learn complex patterns from data, making them powerful tools for tasks like image recognition or language processing.
Deep neural networks require large amounts of training data, though. The more examples you give it, the better the system is at doing its job. That raises the important question: how do you get access to these services?
You could start your own by paying for deep-learning software, but that might not be feasible for everyone. And even if you did, there’s no guarantee people will use your trained model instead of one provided by someone else.
So some companies have designed their products as a kind of premium service: users must first pay a monthly fee, for example, before they can use the tool.
Google makes millions
With every new search engine or social media platform, there’s always an incentive for companies to develop applications and services that will help them gain more users. This is how most big tech companies make their money — by creating products people use to connect with each other and improve the experience.
By offering these apps as part of your online service, you get credit for helping others use it and thus boosting your reputation in the community.
Google gets paid not only through advertising, but also through its own internal apps and tools like Gmail and Maps. These earn it even more kudos from users.
The best example of this is YouTube. It doesn’t charge advertisers to put up videos, but it does offer sponsorships to content creators who want to promote themselves using the tool.
This way, both parties benefit! For YouTube, it gains exposure while the influencer benefits through increased followers and engagement. For the advertiser, they get targeted feedback about what messages work and don’t work when promoting brands.
The future of AI
Artificial intelligence (AI) has become one of the most popular buzzwords in recent years. Technology companies have made heavy investments in this field, promoting it as an integral part of their business model. While there is no denying that technology powered by AI can do some incredible things, there are also concerns about how quickly it is advancing and whether it will result in mass unemployment or even warfare.
There is a common misconception about what AI actually is. Most people associate AI with computer programs which perform logical calculations or manipulate information according to rules. This is not the only use case for AI, however!
In fact, experts now refer to applications which don’t necessarily rely on complex algorithms as being “artificial intelligent.” Some examples of areas where AI is already used include:
Self-driving cars
Chatbots
Voice recognition software
Computer vision systems such as those designed to recognize objects
All of these technologies require large amounts of data to be processed, test hypotheses, and learn from mistakes. If you’ve ever used Google Maps or any other mapping app then you’ve experienced just how powerful computers can be when given enough information.
Artificially intelligent systems are simply machines which employ similar concepts to humans. They process information, make assumptions based on past experiences, and apply logic to solve new problems.
Popularity of the Google search engine
A lot of what makes the top-ranking engines like Google popular is that they have lots of users. By having more users, they get more information to use as features in their algorithms to determine which websites should rank higher and lower.
By using all these different pieces of data, the engine can compare one website with another and work out how good or bad it is for you, its advertisers, and the company itself.
Google uses this information to create an ever-changing ranking system that updates every few minutes! This is why people love the speed and quality of the service that GOOGLE offers.
There are also rumors that Google uses this knowledge to strengthen their position as the number 1 search engine in the world by investing heavily in advertising on other sites and creating fake competing services to push down weaker ones.
Potential downsides
Recent developments with AI have sparked significant discussion about whether or not this technology is inherently good or bad. Some argue that advanced algorithms can be designed to do very harmful things, and even go so far as to call it an epidemic that we need to address now!
There has been growing concern over how powerful these systems become, and how they are able to learn complex sets of rules without being corrected by someone else.
As more and more companies use AI for both predictive analysis and automation, there are risks involved in software that isn’t properly vetted.
Researchers are also raising flags about ethical issues when it comes to creating technologies that monitor and manipulate human behavior.
Given enough data, machines will eventually outperform humans at almost any task. This poses serious questions about what jobs people will be paid to perform and who will get left out.
In fact, some experts predict that within 15 years every job will be automated, leaving only those paying tribute to God and science.
Who else makes money?
While most people associate AI with things like self-driving cars, some companies are developing AIs that require less oversight. For example, an AI can watch videos and identify if something seems fraudulent or not. Or it could analyze social media to determine whether someone is in need of help or if they should be investigated for crimes.
Another area where AI comes into play is research. Scientists develop algorithms that look at lots of data to find patterns and insights. This includes studying medical records, credit card statements, and other sources.
The growing use of AI has led to new opportunities for professionals. For instance, there are now many ways to make money by helping businesses use technology to improve their performance. These include doing market research, finding better software solutions, and teaching others how to do the same.
By offering your services as cost effective alternatives to expensive ones, you can earn significant income without having a degree or specialist training.
