How to use Machine Learning (ML) in a custom mobile app?
What is Machine Learning? How to use Machine Learning in Custom App Development? In this article, we will cover these and a list of other related topics. Join us as we take a look at artificial intelligence, machine learning, and deep learning and how they can help new businesses to grow.
In the 1970s, most computers were the size of large rooms and could only function when told what to do by a skilled programmer. Fast forward to the 2020s, and computers are not only a lot smaller but a lot more clever.
Intelligent computers and systems are on the rise, helping automate repetitive tasks and making life easier for people across the world. In fact, it’s estimated that the market will be worth an incredible $125 billion by 2025, a staggering 2,400% increase over ten years!
If you’re considering launching a startup this year, this means that utilizing the power of Machine Learning in Custom App Development could be a savvy business move.
Not sure what you need to do to get ahead in the Machine Learning market? Don’t worry – we’ve got you covered.
Table of contents
- What is Computer Intelligence?
- What is artificial intelligence (AI)?
- What is Machine Learning (ML)?
- What is the difference between artificial intelligence and machine learning?
- What is deep learning, and how is it different from machine learning?
- Our top tips for launching a Machine Learning-based startup
- In conclusion: think smart when it comes to Machine Learning in custom app development
- FAQ
What is Computer Intelligence?
Computer Intelligence is when a computer or system learns how to carry out a task. It can be learned by watching someone else do it or by using data.
Machine Learning in custom app development is sometimes referred to as artificial intelligence or AI for short. However, there are some other subsets of Machine Learning too, which we will go into more detail about later on in this article.
What is artificial intelligence (AI)?
Probably the most well-known type of Machine Learning in app development, AI is all about smart machines that can conduct specific tasks. Most importantly, it’s about computers carrying out tasks by thinking like humans would.
You may think that AI is something straight out of the pages of a science-fiction novel. But the reality is, that we’ve all been using artificial intelligence for many years.
Smart assistants on our phones like Siri and Cortana are all powered by AI, as are the smart speakers many of us have in our homes and offices. When we visit websites, chatbots operated by AI are there to answer our most commonly asked questions. Even our email spam filters use AI. They are learning which keywords and phrases to look out for and getting more efficient over time.
Where is artificial intelligence used?
As well as the examples above, artificial intelligence can be found across a wide range of different industries. Here are just some of the ways this technology is used.
AI is being used in the healthcare industry to diagnose diseases more accurately. Quite often it is used for training robots to carry out delicate surgical procedures. AI also played a crucial part in the pandemic, being used in thermal imaging across airports and recognizing COVID-19 infections in body scans.
Artificial intelligence is being used in the digital marketing industry to help write website content, optimize websites and recommend products and services to people. As a result, 30% of sales and marketing teams claim to have seen between a 6 and 10% increase in revenue after implementing AI across their businesses.
AI has been used in manufacturing for many years, automating repetitive processes, keeping track of inventory, and ensuring high levels of quality on the production line. Six out of ten manufacturing companies use artificial intelligence in some shape or form.
Examples of modern-day startups using AI
As you can appreciate, artificial intelligence is an extremely lucrative industry to get into. It’s just about thinking of the right idea and getting it to market.
Here are some of the latest startups that are using AI to improve people’s lives for the better.
- Aurora. Aurora is using AI and machine learning (more on that later) to create algorithms for self-driving vehicles
- Dragonfly. Dragonfly is a UK-based startup using AI to see what type of content grabs people’s attention, both online and offline. This helps retailers improve their marketing strategies
- Fireflies. Fireflies is an AI assistant that can be used to record and transcribe meeting minutes automatically. This can be a great way to save time and ensure any meetings stay productive
- Luminance. Luminance is AI software designed specifically for the legal profession. It helps lawyers carry out contract reviews, highlighting anything of interest and making sure no critical information is missed out
- Odin Vision. This startup uses AI software to locate and detect cancerous polyps during medical procedures
- Sonic. This AI software identifies water leaks in people’s homes. It sits under the sink and can automatically shut off the water if it becomes aware of a substantial leak. It can also predict if pipes are about to freeze, sending a warning to property owners
What is Machine Learning (ML)?
Machine learning is all about using data and algorithms to imitate humans. The term was devised in the 1960s by IBM. The company used data to create a computer-based version of the board game checkers (or draughts as it is known in some countries). In 1962, a checkers expert played the game and lost to the computer.
Machine learning comes in three different forms: supervised, unsupervised, and reinforcement. Supervised learning is when the data is labeled, and unsupervised learning is when the data is not labeled. While supervised learning is the most commonly used and can help systems become more accurate, unsupervised learning is good for finding patterns that people may not be looking for.
Finally, reinforcement learning is when an AI system receives a reward or punishment when it gets feedback on right or wrong.
If you want to find out more about the different types of machine learning. There’s a great blog on the NVIDIA website for this.
Some examples of machine learning that are currently in use today include search engine algorithms and face recognition on social media.
What is the difference between artificial intelligence and machine learning?
Machine learning is technically a subset of artificial intelligence. Both types of Machine Learning are concerned with creating programs and algorithms that can imitate humans.
AI is about creating computer systems that can think like humans. At the same time, machine learning is about extrapolating data to help systems learn.
A simple way to remember the differences is that machine learning powers the systems that AI creates.
What is deep learning, and how is it different from machine learning?
If machine learning is a subset of AI, then deep learning is a type of machine learning.
Deep learning helps computers and other systems think using structures modeled on the human brain.
While the long-standing medical fact that we only use 10% of our brains is actually a myth, indeed, we could all stand to utilize our brains in more efficient ways. Deep learning helps computers to think more like humans do. But this way unlocks their potential in ways machine learning can sometimes find challenging.
How does deep learning accomplish this? Through the use of deep neural networks. These networks imitate the human brain, allowing data to be passed from one node to another.
The benefit of deep learning is that once a system is in place, human intervention is no longer needed, or at the very least, intervention is minimal.
An excellent example of deep learning and how it is used is through chatbots on websites. Modern-day chatbots can decipher ambiguous questions, either answering them directly or passing the customer on to a human for further clarification.
Find out how to build your own intelligent chatbot using deep learning.
Our top tips for launching a Machine Learning-based startup
If you’re thinking of launching a startup that utilizes Machine Learning, there are a couple of things that you need to consider.
Here, we would like to share our top tips for making sure your new startup idea will be a success.
Get the right team in place to develop Machine Learning in a custom mobile app
A good team can be the difference between your startup succeeding and having to go back to the drawing board.
As well as a strong team of software engineers, data analysts, and programmers, you will need a good marketing and sales team too. This industry is extremely competitive, so you need people to determine your unique selling point and promote your startup to potential customers.
Think about your own personal strengths and weaknesses, and who you can hire to counterbalance them.
Think about your funding before developing Machine Learning in a custom mobile app
The problem with Machine Learning technology-based startups is that they can be expensive to run. Not only do you have to think about staff, but you need to dedicate a lot of time to research and development too.
If bootstrapping (financing your own startup) isn’t an option, you can either look at grants or investments. The good news is that there are many different grants available for businesses working in the digital technology sector, so be sure to do your due diligence.
If you want to go down the investment route, many venture capitalists and angel investors are focusing on the AI market. There is a lot of interest in AI at the moment, with investors spending nearly $30 billion this year so far on AI-related startups.
So, get writing those emails and start preparing a killer pitch deck!
Know your target audience
Like all startup ideas, it’s important to know to who you’re selling your product or service.
While products like Alexa and Google Home appeal to a broad demographic, it’s better to narrow down and focus on a specific target audience. Think about who they are, what other technologies they use, and how your product or service can alleviate their pain points.
Utilize lean startup methodologies
It’s something we’ve mentioned several times on this blog. But the lean startup approach can help you get your product to market quicker and more cost-effectively.
In the competitive world of computer, intelligence uses custom app development, which can help give you the edge over any potential competitors in your chosen market.
In conclusion: think smart when it comes to Machine Learning in custom app development
We hope this article has given you more of an understanding of Machine Learning and all the different subsets like artificial intelligence, machine learning, and deep learning.
For an example of a Machine Learning company that has found true success, take Soundhound. The audio and speech recognition startup was founded in 2005. Despite initially struggling against its nearest competitor, Shazam, the business grew and grew, due to strategic funding and some clever business deals.
In November of this year, it was announced that Soundhound would merge with Archimedes Tech SPAC and be publicly listed on NASDAQ this year.
The Machine Learning market can be a great one to get into, and if you have a great idea, you can really reap the rewards. Machine Learning in custom app development can be highly competitive though, which means you either need to be really innovative or do things better than your rivals.
FAQ
Machine Learning is a field of artificial intelligence where computers or systems learn how to perform tasks by utilizing data and algorithms. It involves training systems to make predictions or decisions based on patterns and information derived from data.
Artificial intelligence (AI) is a broader concept that refers to the creation of computer systems capable of performing tasks that typically require human intelligence. Machine Learning is a subset of AI that focuses on enabling computers to learn from data and improve their performance over time.
In custom app development, Machine Learning can be used to enhance the app’s functionality by enabling it to learn from user interactions and data. This can result in more personalized experiences and better predictions within the app.
There are three main types of Machine Learning: supervised learning, unsupervised learning, and reinforcement learning. Supervised learning involves labeled data, unsupervised learning involves unlabeled data, and reinforcement learning involves learning from rewards and punishments.
While Machine Learning offers many benefits, it also has limitations. One limitation is the possibility of “false negativity,” where good ideas may be prematurely rejected based on initial feedback. Additionally, Machine Learning may not be suitable for ideas with potentially serious consequences or critical risks.
Launching a Machine Learning-based startup requires careful planning. Building the right team, securing funding, knowing your target audience, and utilizing lean startup methodologies are essential steps for success in this competitive market.
Yes, there are several successful startups utilizing Machine Learning. Examples include Aurora, which focuses on algorithms for self-driving vehicles, and Fireflies, an AI assistant for recording and transcribing meeting minutes. These startups showcase the diverse applications of Machine Learning.
To stay competitive in the Machine Learning market, focus on innovation, unique value propositions, and continuous improvement. Keep track of industry trends, invest in research and development, and be prepared to adapt to changing customer needs.
Looking to launch your Machine Learning-based startup? You are launched is here to help
We’ve been working with startups since 2016, giving them the support and advice they need to grow
If you’re looking to launch your new startup in the year ahead, whether artificial intelligence, deep learning, or machine learning, we have the experience needed to get you started.
Contact us today to find out more about the services we provide and how we can get you launched this year.
