The development of Artificial Intelligence technology was thought to be rapid, of course. But in the last few years, there has been an exponential increase in the number of platforms, applications, and tools based on machine learning and artificial intelligence technologies.
Scientists and developers continue to design and develop intelligent machines that can mimic reasoning, develop and learn knowledge, and attempt to mimic how humans think.
Of course, it is really difficult to follow technologies that are developing so fast. For this reason, in order to keep up with these technological developments, we tried to gather Top 10 Artificial Intelligence Technology trends for you.
10. Privacy and Policy
The General Data Protection Regulation (GDPR) came into force on 25 May 2018. The GDPR is the toughest privacy and security law in the world. Though it was drafted and passed by the European Union (EU), it imposes obligations onto organizations anywhere, so long as they target or collect data related to people in the EU. These and similar regulations are a must today at a time when too many people entrust their personal data to cloud services and breaches occur on a daily basis. Considering that the firm stance on data privacy and security is more important than ever before, it is possible to mention that such measures are evolving as fast as the pace of technology change. It’s not too hard to understand how important privacy policies and security will become when robots and artificial intelligence are involved. In this context, the important thing is to learn the emerging regulations, to deal with security and privacy technologies means to master these policies that will dominate in the coming years.
9. The Convergence of IoT, Blockchain and AI
In order for artificial intelligence to become a more efficient technology in every field, it is necessary to adopt a structure integrated with other technologies. For example, self-driving cars may not make much sense without the Internet of Things (IoT). But when the two are together, it is literally a technology.
IoT activates and regulates the sensors in the vehicle by collecting real-time data, whereas artificial intelligence models are just what the vehicle needs to move on its own.
Likewise, Blockchain*; security can work closely with AI to troubleshoot scalability issues. It has to work in the near future. Because it is inevitable to secure Big Data collected by IoT and artificial intelligence algorithms.
What is Blockchain?*
Blockchain is an ever-growing list of records, called blocks, that are linked and secured using cryptography. It is the technology that powers bitcoin.
8. Facial Recognition Technology
Facial recognition systems are a reliable method of biometric authentication. Facial recognition technology is the process of identifying or verifying the identity of a person using his or her face. Captures analyze and compare patterns according to the person’s facial details, mimics. Due to large investments and R & D studies in this area, the accuracy and legibility of artificial intelligence applications belonging to facial recognition systems have gained a lot of importance in the last few years. According to the NIST (National Institute of Standards and Technology) Report, massive gains inaccuracy has been made in the last 5 years (2013- 2018) and exceed improvements achieved in the 2010-2013 period.
Most of the facial recognition algorithms in 2018 has evolved to outpace the most accurate algorithm as of late 2013. Considering that only 0.2% of the searches in the 26.6 million photo database were incorrect in 2018, this ratio was recorded as 4% in 2014. It’s a 20x improvement over four years. It shows us how important this technology will become in the future.
If we also think that technology giants like Google, Apple, Facebook, Amazon, and Microsoft are investing a lot of money in these areas, it can be said that in the near future, with the help of AI, the usage and development of facial recognition technology will increase considerably.
7. Artificial Intelligence (AI) Assistants
Artificial intelligence assistants can simplify, automate, or even take away the jobs of employees in ordinary customer service and sales departments! We already have popular assistants like Siri, Cortana, Alexa, and Google Assistant. Many companies, especially technology companies, have started to use artificial intelligent assistants to perform basic tasks and develop them for various tasks.
It seems that in the coming years we will continue to see artificial intelligence assistants more in every aspect of our lives. By 2020, ComScore predicts that about half of all searches will be made by voice. Even this data can give us an idea of how much AI Assistant technology will improve in the future.
6.Intelligent Automation in the Workplace
Imagine that you order dinner in a restaurant and that order is served by robots. It may seem like a dream right now, but in the near future, they will become reality. Imagine how this technology can work in workplaces and production facilities!
Intelligent automation takes process automation to a completely different level. Instead of just implementing and processing predefined steps, automation that works with artificial intelligence is becoming more and more self-evolving over time. When performing tasks and workflows, data obtained during processes can be used for process improvement purposes automatically in the future.
Intelligent automation can also help identify and optimize best practices that enable managers to make more decisions that are more data-driven and therefore accurate. Although the development of this technology seems slower than others, we can say that we will continue to see it in certain areas of the business in the coming years.
5. Artificial Intelligence in the Media and Entertainment Industry
Artificial intelligence (AI) does not seem to have penetrated the media and entertainment industry much, but the fact that last year, the world’s first artificial intelligence anchor presented the news proves to us how much different areas of artificial intelligence have spread.
4. Artificial Intelligence in Cybersecurity Systems
By the day, the complexity and scale of cyberattacks are increasing at a faster rate, and current defensive measures remain inadequate. When we wake up one morning, we can get the news that a virtual store we shop at has been cyber-attacked and that everyone’s credit card information has been stolen.
It is, of course, a sensible approach to hand over the protection of such big and important data against cyber-attacks to a superhuman algorithm.
Therefore, artificial intelligence cybersecurity systems will continue to play an important role in managing these attacks in the coming years. Using machine learning, organizations will be able to easily detect such security breaches and ensure that information security authorities take necessary measures in advance.
3. Intelligent Applications
Intelligent applications are enterprise applications with embedded or integrated artificial intelligence technologies to support or replace human-based activities via intelligent automation, data-driven insights, and guided recommendations to improve productivity and decision making.
Today, enterprise application providers incorporate AI technologies into their offerings and at the same time provide artificial intelligence platform capabilities from enterprise resource planning (ERP) to customer relationship management to human resource management and labor productivity applications. Managers should challenge their packaged software providers to outline in their product roadmaps how they are incorporating artificial intelligence technology to add business value in the form of advanced analytics, intelligent processes, and advanced user experiences. It is possible to say that any application produced as a solution to any problem will be equipped with artificial intelligence, and the companies with this perspective will make a difference in the future periods in which the application will increase the efficiency in the field where it is used.
2. Artificial Intelligence Chips
Leading chip manufacturers such as Intel, Nvidia, AMD, and ARM are aiming to produce AI-powered chips to speed up the operations of applications that run on AI. An artificial intelligence application relies on the processors as it needs significant speed.
Because an artificial intelligence model needs considerable speed, the performance of the processors is crucial for the efficient operation of these applications. It’s quite difficult for the CPU systems to complement such applications when the processes involve facial recognition and object identification which require complex mathematical computations in parallel. In the coming period, performance improvements in artificial intelligence chips, NLP (Neurolinguistic Programming), speech recognition, computer vision and so on applications are designed to run at full capacity and are thought to be an excellent solution to this problem.
1. Automated Machine Learning (AutoML)
We can say that this technology, which is thought to revolutionize business intelligence, will be a more talked-about term in the coming months. Complex problems have the goal of solving them without manually training the machines through the typical process of training models. Instead of getting stuck in the process of training machines, business intelligence specialists will be able to focus much more on their core issues.
What is AutoML?
Automated Machine Learning (AutoML) is the process of automating the end-to-end process of applying Machine Learning (ML) to create, develop and deploy predictive models so that any enterprise benefits from data. It mainly focuses on two main issues – data collection and forecasting. All other steps that take place in between can be easily automated while providing a model that is well optimized and ready to make predictions.
An example is Microsoft’s recent addition of automated machine learning to Power BI, a business intelligence solution. In Power BI, AutoML allows data analysts to use data streams to create machine learning models with a simplified experience using Power BI skills. Much of the data science underlying the creation of machine learning models are automated with Power BI.
AutoML has protections to ensure that the generated model is of quality and provides visibility of the process used to build your machine learning model. As a result, we can also say that technology companies have given the first signs that they will invest more in the AutoML field in the coming period.
(This blog was originally published here)