Table of Contents
AI is a broader subject, while machine learning and deep learning are specializations.
What is Artificial Intelligence or A.I.?
Artificial Intelligence is the theory and development of computer systems that enable the performance of tasks normally requiring human intelligence such as visual perception, speech recognition, decision-making or even translation between languages. Coined in the year 1956, AI is more prevalent today than it ever was, due to the wide availability of high speed computing power. Processing speeds have improved to a point where quantum computing is a reality.
AI has many uses. Apple’s Siri is an example of artificial intelligence helping humans schedule meetings, and perform simple tasks like playing music. AI also has many uses in business. For example, it can be used to process automation, automated customer support or even something as simple as spam filters. Companies like Tesla are using AI for their self-driving cars. Therefore, AI is a very exciting field for companies as well as their customers.
What is Machine Learning?
Machine learning is the scientific study of algorithms and statistical models that computer systems use in order to perform a specific task effectively relying on patterns and inference. Machine learning is a subset of artificial intelligence. If machines can be taught to automate tasks and replicate human behavior, they can also be taught to spot patterns in data similar to how a human would be able to.
Statistics is often used to train large complex models. Machine learning is used in heavy manufacturing production plants to reduce defects in produced goods. The system is enabled to learn from previous mistakes and identify areas in the manufacturing process that are deteriorating or causing mishaps. The repair and maintenance teams are then fed these reports to help fix the machinery so that production doesn’t stall.
What is Deep Learning?
Deep learning is a form of machine learning that is based on artificial neural networks. Here the learning can be supervised, semi-supervised or unsupervised. This is where we are on the fringes of science fiction. Jeff Dean, the current lead of Google AI said, “When you hear the term deep learning, just think of a large deep neural net. Deep refers to the number of layers typically and so this is kind of the popular term that’s been adopted in the press. I think of them as deep neural networks generally.”
A neural net, in this case, refers to an artificial neural network or a connectionist system inspired by biological neural networks that constitute animal brains. The idea is that the system will learn to perform tasks by considering examples without being programmed with any task-specific rules. With brain simulations and huge amounts of data, deep learning researchers are able to develop better algorithms and gain newer insights.
Deep learning has some amazing applications in healthcare, voice search, voice-activated assistants, image recognition, advertising, disaster prediction, and energy or power usage and management. In short, deep learning is a very specific specialization of machine learning.
Stay ahead of the game with our helpful resources
4 digital solutions to address common application performance issues
High network latency, memory leaks, slow page loads, heavy CPU usage, and unresponsive servers are all typical performance issues we’ve experienced at some point when using or accessing digital applications. With how easy they occur in projects across verticals, you might be wondering whether the development teams behind these programs have done enough due diligence prior to the release. But human errors and oversight aren’t always the culprit. The reality is that while developers can strive to develop a fully functioning program with virtually no apparent faults upon delivery, no software is truly error-free. Even the most rigorously tested applications
6 useful tips for creating more robust application lifecycle management
As digital technology becomes the norm, software acquisition is now key to gaining a competitive edge in today’s market. Be it as a value offering tailored to consumers or a productivity tool to run complex processes, custom software undeniably helps companies drive growth and deliver value more efficiently. Just as necessary as having a proprietary application is prescribing a standard procedure to govern and maintain its utility. This is to ensure that your business can develop or adopt the right type of software—one that can fully cater to your business needs while keeping disruption to a minimum across critical milestones.
5 major roadblocks businesses must overcome when transitioning into a new software environment
As the business landscape becomes increasingly saturated, staying ahead of the curve often means embracing disruptive technologies to meet the fickle market demands. In most cases, this entails knowing when to pivot your current strategy to an entirely new solution. But recognizing the importance of digital shift is one thing; implementing the necessary IT upgrade is another. A global survey by Deloitte has found that although 87% of companies manage to identify the impact of digital trends on their industries, only 44% have adequately prepared for the coming disruptions. This vast disconnect between organizational expectations and conditions in the field
Is cloud computing the answer to better software development?
Cloud computing is perhaps not a term often heard in daily conversations, but it is one with a far-reaching impact on our technological needs. From expansive options of online data storage to numerous suites of web-based productivity tools like Google Workspace, nearly everyone has used a cloud-enabled technology. Over the last decade, this high degree of versatility also underpins the rapid cloud uptake among businesses. In fact, one survey has found that 94% of companies have already shifted their computing workloads on cloud platforms to varying extents. Unsurprisingly, the market size for cloud technology continues to grow exponentially. With a
Sindhu
Client Success Manager
Sindhu is a tenacious and impassioned digital product and project manager specializing in driving client success across complex healthcare technology implementations and integrations. She is a certified Agile Scrum Master and holds advanced degrees in computer science and software engineering. Her philosophy is that “work is where the heart is” and believes the key to success is creating a solid, supportive, and cohesive team.