In the year 2020, there were many challenges, but there was also a unique opportunity to leverage technology on multiple fronts. From adopting it in various industries such as retail, eCommerce, and others, ensuring worker safety in remote work scenarios and improving consumer experiences has taken on many forms. With the adoption of data analytics, artificial intelligence, cybersecurity, and other new technologies, the business scenario’s changes have exponentially multiplied.
Based on last year, 2021 has the potential to grow newer tech trends. Intelligent machines, hybrid cloud adoption, increased NLP adoption, and data science and AI adoption will be highlighted in the coming year. The next year may also witness pragmatic AI trends, containerization of analytics and AI, algorithmic differentiation, augmented data management, differential privacy and quantum analytics. Given these trends, we can conclude that data is increasingly becoming an integral part of organizations, especially after the pandemic.
IT’S TIME TO BE ARTIFICIAL
Without further ado, let’s examine tech trends
1.Embedded artificial intelligence chips
Artificial Intelligence often involves mathematical computations for speeding up specific tasks, such as facial recognition, voice control, object detection, and natural language understanding. Completing these calculations requires additional hardware, which is where specialized processor chips come into play.
If a high-performance processor can’t handle and increase an AI model’s speed, we are afraid it cannot! As a result, the chip giants like Intel, AMD, NVIDIA, Qualcomm, and ARM develop dedicated chips for AI app execution.
However, it is most likely that these chip-enabled AI applications will be used in the automobile and healthcare industries to deliver smart solutions to their customers.
Another upcoming development is the massive investment in custom chips based on ASICs and FPGAs by reputed companies like Microsoft, Amazon, Facebook, and Google. These companies are investing heavily in this area.
The processors will be compatible with the High-Performance Computing (HPC) standard, which will enable them to assist in predictive analysis and query processing
2.Reinforcement Learning & Deep Learning
Deep learning is an automated system that can identify patterns from existing data to train algorithms and then apply those algorithms to new data to predict the outcome. It is essentially a self-teaching system that uses input data to create a predictive model capable of analyzing and interpreting new input data.
Deep learning algorithms aim to achieve this by using layers of neural networks. Neural networks are designed to mimic human brains – they store digital data in various formats for evaluation and classify information.
Techniques used in reinforcement learning include image recognition, voice control, robotics, natural language processing, autonomous vehicles, and automatic text generation. Reinforcement learning takes a different approach than conventional algorithms, using sequential decisions instead of data recognition and classification techniques. Examples include:
Intelligent Personalized Tutoring Systems.
Treatment policy optimization for chronic illnesses.
LOXM, a trading platform.
These are some of the most commonly encountered trends in machine learning.
3.Facial Recognizing Machine
This technology uses biometrics to map facial features. It then compares the mapped prototype to a database to determine a match.
It was first used to search for criminals in crowds in Tampa in 2001.
The first time that facial recognition was used in mobile devices was in 2005. Since then, technology has advanced by leaps and bounds. Although a setback in the form of a Facebook data breach caused negative comments last year, research and continual improvement have proven to be the saving grace!
Facebook’s Deep Face program enables users to tag people in photos, and it recognizes people from uploaded photos. Google won a lawsuit last month, and Chinese AI start-up giants Megvii and SenseTime are making ground in facial recognition.
4.Internet of Things (IoT) and Artificial Intelligence (AI)
Internet of Things (IoT) is a technology that most people use but do not fully understand. It is basically a system made up of sensors linked to a central server. Sensors communicate without the need for a user and act automatically based on specific data sets.
Smart cars, electronic appliances, vending machines, smart speakers, connected security systems, smart lights, and thermostats are some common examples of IoT objects. Can IoT function without AI? You’ll find AI applications in every IoT system, just in varying degrees. A self-driving car, for example, cannot function without a close interdependence between IoT and AI.
We use machine learning models capable of handling speech synthesis, video frames, time-series data, and other unstructured data from microphones, cameras, and other sensors as part of the industrial Internet of Things.
5.Artificial Intelligence & Digital Ethics
Digital ethics is becoming more and more relevant today, and for a good reason! More people are becoming aware of the potential threats of storing their personal data on private cloud servers of private organizations, and we can observe it from the recent developments in various associations as well as the government.
What should be ensured is that AI applications are designed ethically. A fully autonomous, AI-powered environment is very appealing, but do these environments imbibe social values? Are AI developers doing enough to deal with this ethical dilemma?
Legal accountability and responsibility when an AI system causes harm.
Transparency regarding the policy of data usage, the rules of AI systems, as well as audit trails.
There should be policies in place to handle the implications and impacts of Artificial Intelligence systems.
Ensuring that fundamental human rights are not violated through governance frameworks.
Prohibitions and obligations can be expressed computationally to embed values in AI systems.
Artificial Intelligence Trends – A Challenge
Knowledge of the inherent challenges regarding AI is essential. Knowing the problem areas will aid in understanding what it takes to make AI relevant and useful.
1.Lack of Uniform Regulations
After the recent Facebook privacy worries and the GDPR introduction, people understand how important usage consent is. Different countries should come together to form a uniform set of AI regulations.
2.Socio Economic Issues
The World Economic Forum released a report discussing the impact of AI on employment. Some think it will improve employment, while others think it will undermine it.
3.Data Validity
Biased data input often leads to biased results in machine learning models used for decision-making that often impacts mortgage loans, recruitment, social services, and more.
When an organization attempts to deploy newly developed artificial intelligence systems and machine learning models, it often faces challenges such as system maintainability, scalability and governance. Consequently, AI initiatives often fail to meet their goals.
AI engineering should be a disciplined process incorporating elements of DataOps, ModelOps, and DevOps. Gartner says AI should be a part of the DevOps process, not a set of isolated projects.
Artificial intelligence and machine learning are increasingly being used in cybersecurity applications for corporate systems and home security.
Cybersecurity developers are constantly updating their systems to keep up with constantly evolving threats such as malware, ransomware, DDS, and more. Artificial intelligence and machine learning technology can be employed to help identify threats, including variations of earlier threats.
Furthermore, AI-powered cybersecurity tools can collect data from a company’s transactional systems, communications networks, digital activity and websites, and external sources and use AI algorithms to identify patterns and identify threatening activity, such as identifying suspicious IP addresses and potential data breaches.
Conclusion: The COVID-19 pandemic has crippled economies worldwide, and many are struggling for survival. Businesses and institutions should increase technological advances to survive the storm, but innovative approaches are necessary to survive it and rebuild. Researchers and scientists are continually extending the number and scope of AI-powered applications. Artificial intelligence is impacting every industry and every human being. It has led to technologies such as robotics, big data, and the Internet of Things, and it will likely continue to act as a technology innovator in the future.