Artificial Intelligence (AI), Machine Learning (ML) Bots, Deep fakes, and Facial Recognition are the other aspects of AI that have swept us up in a tidal wave of transformation that has irreversibly altered the lives. However, the modifications were incompatible. Others help to create unrivaled marvels in fields like healthcare, defense, education, marketing, and more by compromising safety, freedom, and privacy.
A few decades ago, the idea of computer programs studying, evolving, and making decisions on their own was the stuff of science fiction. Their strategies are now rooted in every aspect of our lives.
As AI evolves and improves at an exponential pace, we must determine its effect on our lives through industries and countries. So, here are the six patterns to keep an eye on in 2021.
AI-Driven Education System
AI can play a role in the development of educational tools, the simplification of administrative tasks, the creation of smart content, the personalization of learning, the crossing of state borders, and much more. Do our students, those whose lives are affected by AI, have a basic understanding of the technology?
Most of the public are unaware of the implications of this technology and are poorly engage in AI-related debates. Sure, AI-related information is readily available on the internet, but not everyone has access to high-speed internet, such as Spectrum internet, and some are simply not interested in it. We need to include AI subjects in our curricula.
However, that is expected to change in 2021, as EU AI experts call for compulsory AI education. Finland’s government has initiated an initiative to teach AI fundamentals to 1% of the country’s population.
The inception of AI and its related Laws
Following the 2016 presidential election, it became clear that AI’s ability to influence people’s perceptions and decisions was risky. This was a huge wake-up call to lawmakers to take action and curb any such future instances.
Many state laws have since been enacted to protect the public’s interests. For example, California passed a law requiring social media platforms to impose a bot disclosure policy on companies operating on their platform so that users can tell whether they are communicating with an automated bot or a person.
Optimizing Healthcare Resources
AI is having a huge influence on the healthcare sector. Its ability to collect data from different locations and resources, then translate and analyze it rapidly and meaningfully is not only helping to streamline workflows for patients and healthcare professionals but is also attempting to cut costs.
This capability may not be limited to improving healthcare processes such as automated reporting, scheduling, resource distribution, and equipment use, but the computer software could be our only hope for coping with deadly outbreaks that afflict the entire planet. The year 2021 may be a game-changer as healthcare practitioners, and computer scientists compete to contain the COVID-19.
AI Requiring Less Data
Deep learning subsets of AI-ML necessitate a massive amount of data for training in order to be more accurate in their performance. Apart from the big five, Google, Amazon, Microsoft, Apple, and Facebook, most businesses find it difficult to gain the right type of data for their operations, particularly in large quantities. Data synthesis may be the panacea for AI’s insatiable demand for data, as well as the data challenges that businesses face. Companies are experimenting with different data-synthesis methodologies in order to produce new data from existing data, removing the need to collect large volumes of real-world data.
There have been advancements in data synthesis in the field of generative adversarial networks (GAN), and several areas have succeeded in the process.
The collection of real-time driver data proved to be a significant obstacle to automotive companies’ ambitions for a customized driver experience, but the industry continues to build highly advanced tailored driving features thanks to data synthesis and characterization.
Well-Developed Neural Networks
Neural networks have become more advanced in terms of depth and proportions, as well as their methodologies and efficacy. Neural network algorithms will soon process data and produce findings that are nearly identical to those of the human brain.
Deepfakes are increasingly realistic in their voice, face, video, image, and text replications, resulting in a wave of fake news on social media sites.
Grover, a detection system developed by the Allen Institute, has proven to be very successful in detecting deep fake material. The generative adversarial networks, on the other hand, create deep fake content at a much faster rate than their countering tools, so these tools are still fighting a losing battle.
The sophistication, reach, and control of cyber adversaries backed by AI has posed a greater threat to cybersecurity in recent years. According to Juniper Research, AI-powered cyberattacks could cost businesses $5 trillion by 2024.
To combat this, more companies will introduce AI-assisted security tools. Machine Learning is capable of not only detecting inconsistencies in data easily but also accurately predicting assaults. As a result, rather than being reactive, the defense would be proactive.
Most biometric authentication systems use facial recognition technology, which uses AI software to generate facial models by analyzing similarities and key patterns in the target subject’s face while also taking into account various backgrounds, lighting, hairstyles, and accessories. As a result, the risk of biometric authentication violations is reduced.
Furthermore, the AI has improved its response time in dealing with phishing threats, as it now detects, monitors, and blocks over 10,000 active phishing outlets around the world.
When we see technology working and changing lives around the world, some for the better and some for the worse, relying on their instigator, 2021 is undeniably the year of AI.As the year progresses, we may see AI in our school curricula, as well as in the battle against the novel coronavirus outbreak, government and corporate security, more deepfakes distorting political viewpoints and their detection tools, and deep learning becoming more accurate with fewer data.