AI vs. Deep Learning | eWEEK | Tech Rasta


Deep learning has become the catchphrase-du-jour. It’s a trendy term being used to address the latest wave of artificial intelligence (AI) technologies. Let’s see what it is, how it compares to artificial intelligence and how it is applied.

See also: Top AI Software

What is Artificial Intelligence?

Artificial intelligence can be thought of as the opposite of human intelligence. If living beings are born with natural intelligence, man-made machines can be said to possess it artificial intelligence. So any “thinking machine” has artificial intelligence.

In practice, however, computer scientists use the term artificial intelligence to refer to machines that perform a level of thinking that humans have taken to a much higher level.

Computers are very good at doing calculations – taking inputs, manipulating them and generating outputs using algorithms. But in the past they could do other kinds of work that humans excel at, such as understanding and creating language, recognizing objects by sight, creating art, or learning from past experience.

The development of artificial intelligence is an attempt to change that and give computers exceptionally advanced, human-like intelligence.

Also see: What is Artificial Intelligence?

What is Deep Learning?

Deep learning can be defined as a form of artificial intelligence based on neural networks. It is based on what researchers believe are some of the native abilities of the human brain.

Also, deep learning uses multiple processing layers to extract progressively better and more high-level insights from data. In essence, it is simply a more sophisticated application of AI platforms and machine learning.

How does deep learning relate to artificial intelligence?

Many view deep learning as a subset of both AI and machine learning. They see AI as a broad topic and distinguish the technical factors as follows:

  • AI tries to imitate human intelligence and behavior.
  • In the broader context of AI, machine learning is a technique used to help computers learn using training based on results gathered from large data sets.
  • Both AI and machine learning involve deep learning. This technique is essentially a more complex version of machine learning using neural networks.

Deep learning should be considered a subset of machine learning, which is a subset of AI.

“AI has traditionally been limited to low-complexity tasks and simple decision-making until deep learning came along,” said Peter Boudis, CTO and Chief AI Architect. Rossum. “Deep learning is a type of machine learning based on artificial neural networks.”

He explained that these mathematical structures – loosely inspired by the structure and function of the brain – can be learned by example in a similar way to the way humans learn. The big change is that deep learning will finally allow machines to solve problems of the complexity that humans can solve. It’s responsible for some of the most impressive AI achievements in recent years, Bowdis added.

See also: Future of Artificial Intelligence

Deep learning means bigger, more accurate AI models

A very deep learning excitement is how to scale large simple models, technically called foundational models. Deep learning-based models can also generate images from text and video from text. Therefore, users of AI see deep learning as key to scaling the current limitations of AI modeling.

Algorithms are used to dig deeper into data, preferences and potential actions. They may have the ability to provide versatile answers to complex situations and problems.

“Current models are limited in many ways, and some in the community are quick to point them out,” says Peter Stone, Ph.D., executive director. Sony AI America. “It will be interesting to see what capabilities can be achieved with neural networks alone, and what novel methods can be found to combine neural networks with other AI paradigms.”

But, this is not an instant path to breakthrough insight. Deep learning platforms take time to support data analytics. They have to sift through a lot of data to identify patterns.

The model tries to learn how things work in the real world and thus provides more accurate predictions and accurate analyses. The good news is that advances in neural networking are speeding up the process. Some say the technology will soon reach the same level of maturity as data analytics.

See also: History of artificial intelligence

Deep learning requires hardware and software

It’s not just about using software algorithms, AI and modeling. Hardware is just as important. If you have huge datasets, you need the underlying compute infrastructure to deal with it in near real time. That means a lot of processing power. Graphics processing units (GPUs), for example, require the latest memory fabrics that provide large memory for computing and AI platforms.

“Organizations can improve their AI platforms by combining open source projects and commercial technologies,” said Bin Fan, vice president and founding engineer of Open Source. Alluxio. “It’s essential to consider skills, speed of deployment, different algorithms supported, and flexibility of the system when making decisions.”

Software advancements are also very important. A recent trend is to place deep learning workloads in containers to provide isolation, portability, greater scalability and address dynamic behavior.

“Enterprises will find their AI workloads in more flexible cloud environments with Kubernetes,” Fan said.

Boudis noted the impact of big data on deep learning. He said that the deep learning revolution has become possible with the availability of big data. Deep learning algorithms require large amounts of data to learn effectively. The recent proliferation of data sources such as social media, sensors, and online transactions has made it possible to train deep learning models at previously unimaginable levels.

Some of the most impressive examples of deep learning include “large language models” that can generate clear text on a variety of topics or create realistic images from text prompts. Progress is being announced from time to time.

These innovations quickly permeated business and IT. For example, deep learning is now being used to generate high-accuracy text transcripts from audio recordings of business meetings and phone calls. This saves businesses a huge amount of time as it frees employees from having to manually transcribe these recordings.

“Deep learning is also being used to automatically extract data from business documents with high accuracy,” Boudis said. “This saves businesses a lot of time and money because it eliminates the need for manual data entry.”

See also: Best Machine Learning Platforms

How is deep learning evolving to support AI?

Deep learning has been developing explosively over the past few years. Baudis calls this the fastest growing area of ​​AI. He goes so far as to call the non-deep learning areas of AI a “niche” because of the progress being made on the deep learning front.

But, challenges lie ahead. Goals include:

  • Learning how to do it Achieving superior human performance on the most complex tasks. Progress has been made, but there is still a long way to go.
  • Deep learning needs to get sufficiently robust so that AI tools can become reliable partners for humans; Humans need to know that they can rely on deep learning extensions.

“In some applications, this means that the system can explain the reasoning behind its decisions,” Boudis said. “Among others, it’s about AI learning quickly from its mistakes and adapting immediately to do better next time.”

A practical example in an enterprise might be a document data capture system:

  • First, lower error rates mean a lot of manual work is saved for human operators.
  • Second, whenever a human corrects an AI mistake, the AI ​​must not repeat the same mistake again.

This type of behavior moves software adoption to a higher, more functional level — one that suddenly becomes much less of an IT robot and more like onboarding a new colleague.

AI and Deep Learning Codependency

Deep learning and AI are deeply intertwined at a foundational level. Rick Wagner, senior director of product management at Sailpoint, explains this dependency.

“To deliver artificial intelligence, processes and procedures must learn, generate derived patterns, and even recognize patterns of nuance/differences,” says Wagner. “Deep learning can be used to analyze exceptions when a human intervenes with AI decisions. Those exceptions can be analyzed for patterns, which ultimately improve the effectiveness of AI.

Deep learning drives AI towards new innovations. Artificial intelligence is most beneficial on its own when it can analyze processes and procedures for patterns and exceptions to patterns. To achieve autonomous governance and autonomous identity lifecycle management, machine learning will play a key role with deep learning, providing a predictive level of human-excluded decision-making processes, Wagner said.

“Learned processes and procedures must take place to apply artificial intelligence,” he said.

Shallow AI and Advanced Deep Learning

So, can standard AI be thought of as shallow AI, and deep learning as advanced, cutting-edge AI? Some people think that.

“Unlike shallow categories of AI, a deep learning approach involves continuously ingesting large amounts of data to train a multi-layered deep neural network (DNN),” says Elaine Lee., Principal Data Scientist at Mimecast. “DNN designed to mimic human thought and perception.”

Once a DNN has observed enough labeled data, it can successfully detect or classify new, unlabeled data or even threatening anomalies, thereby alerting users. An integrated AI example. On the cyber defense side, for example, deep learning is being used to detect intrusions or malicious activity and to classify malware and cyberattacks. It also helps organizations harden their AI models, making them vulnerable to attacks that use misleading data.

“The integration of AI, deep learning and algorithms is more important than ever in the cybersecurity world as the most sophisticated threat actors increasingly weaponize their own AI-enabled malware,” Lee said. “Cybercriminals are increasingly relying on the use of social engineering attacks to propagate ransomware, using AI to manipulate bots, manipulate malicious data, create deepfakes, and aid in the scale and effectiveness of their attack campaigns.”

On the combat front, organizations are ramping up their AI defenses to counter the growing sophistication and complexity of attacks. According to Mimecast 2022 State of Email Security Report, 46% of organizations are already using these technologies, and another 46% plan to follow suit. By mimicking human intuition, AI can help detect and block threats more effectively while reducing human error.

Also see: Top Digital Transformation Companies


Source link