This year’s trend is all about hyperautomation, blockchain, Artificial Intelligence security, and everything technology.
The words “Human Augmentation” conjure up notions that seem out of this world, such as advanced high-technology cyborgs. In reality, however, we have been augmenting parts of the human body for hundreds of years now. Everyday things like eyeglasses, hearing aids, and limb prosthetics just evolved into cochlear implants and wearable technology. Even laser eye surgery that was considered bizarre has now turned into a conventional option.
With the constant advances and development in the field of technology, it has now moved on ahead of augmentation that substitutes a human ability and into augmentation that produces superhuman abilities.
So would it be possible for scientists to develop a technology to advance the brain to boost memory, or implant a chip to decode neural patterns? Would it be possible that autoworkers would have exoskeletons as a standard uniform, empowering them to lift weights beyond the average human capacity? Would it be possible for doctors to implant sensors that can monitor how drugs move inside a body?
Whether these developments really happen or not will significantly influence the world. This makes human augmentation one of the 10 strategic technology trends that will make significant opportunities for the upcoming years.
The other trends are focused on how these technologies will transform people and the places that they are in (consumers, employees, homes, businesses, etc.). This is because we believe that in order to further advance technology even further, we have to consider the human side of the world first since at the end of the day, technology is there to make life easier for humans.
- Hyper automation
Automation utilizes technology to automate responsibilities that normally require humans.
Hyperautomation deals with the use of technologies such as machine learning (ML) and artificial intelligence (AI) to rapidly and drastically automate processes and augment humans. This extends over an array of tools that can be automated, but it also applies to the refinement of the automation like discovering, analyzing, designing, automation, measuring, monitoring, and reassessing.
Because of our complexity, no single tool can displace humans. That is why hyperautomation requires an aggregation of tools such as robotic process automation (RPA), intelligent business management software (iBPMS), and Artificial Intelligence, with the goal of improved and advanced AI-driven decision-making capacities.
Hyperautomation also often result in the creation of a digital twin of the organization which allows organizations to envision how functions, processes, and key performance indicators combine to accelerate value. The digital twin organization then becomes an essential part of the automation process as it provides real-time and constant intelligence about the organization and valuable business opportunities.
2. Multi experience
Multi experience replaces people that are great with technology with technology that is greater than people. The conventional concept of a computer evolves from a singular point of interaction to incorporating multisensory and multi-touchpoint technology like wearables and high-tech computer sensors.
A great example for this one is Domino’s Pizza’s app-based ordering that involves self-driving pizza delivery vehicles, a pizza tracker, and smart speaker interfaces.
The multiexperience tech currently focuses on immersive activities that use the combination of augmented, virtual, and mixed reality on top of advanced human-machine interfaces and sensing technologies.
Democratization of technology implies giving people easier access to technical or business expertise without extensive and expensive preparation. The four main focuses are on the areas of application development, data and analytics, design, and knowledge, often dubbed as “citizen access.” The rise in democratization has contributed to the rise of citizen data scientists, citizen programmers, and a lot more.
This technology enabled developers to produce data models without requiring the skills of a data scientist. They could easily make models by using AI-driven development that could generate code and automate testing.
4. Human augmentation
As briefly discussed above, human augmentation is the use of technology to improve a person’s cognitive and physical senses.
Physical augmentation develops an integral physical ability by implanting or hosting a technology inside or on the human body.
The automotive and mining industries use wearables to improve worker safety while industries like the retail and travel industry use wearables to boost productivity.
Physical augmentation has four main categories.
- Sensory augmentation like hearing aids, Lasik, and eyeglasses.
- Appendage and physiological function augmentation like exoskeletons and prosthetics.
- Brain augmentation like neural implants to treat seizures.
- Genetic augmentation like cell and gene therapy.
Artificial Intelligence and Machine Learning are now being frequently used to create decisions instead of humans.
Cognitive augmentation heightens a human’s ability to think and execute better choices like utilizing information and applications to sharpen learning or new experiences. Cognitive augmentation also incorporates some technology in the brain augmentation category as physical implants that deal with cognitive thought.
However, human augmentation brings a series of cultural and ethical implications making it a little more complicated than the rest of the technological trends enumerated in this list.
5. Transparency and traceability
The development of technology is building a trust crisis. As users become more conscious of how their data is being gathered and managed, businesses are also realizing the rising accountability of collecting and gathering data.
Furthermore, since Artificial Intelligence and Machine Learning are also being used more often to settle decisions instead of humans, trust crisis is growing and driving the necessity for designs like understandable AI and AI governance.
Transparency and traceability, therefore, require a focus on six key factors of trust: Ethics, integrity, accountability, competence, openness, and consistency.
6. The empowered edge
Edge computing is a topology where content collection and information processing and delivery are set closer to the sources of the information. The main concept behind this is that having local traffic and distribution will minimize latency. This includes all the technology on the Internet of Things (IoT). The empowered edge studies how devices are growing and developing frameworks for smart spaces and prompts pivotal applications and services closer to the consumers and devices that utilize them.
By the year 2023, it’s possible that we’ll have 20 times as many smart devices at the edge of the network as in traditional IT roles.
7. Autonomous devices
Autonomous devices including drones, robots, and appliances employ AI to complete duties normally done by humans. This technology functions by using intelligence from semiautonomous to completely autonomous under a variety of conditions in air, sea, and land.
As of now, autonomous devices are mainly used in controlled environments like a mine or a warehouse, but experts estimate that they will eventually be utilized in public areas. Autonomous devices will also shift from stand-alone to collaborative groups, similar to the show-stopping drones used during the 2018 Winter Olympic Games.
Now, autonomous devices cannot displace the human brain and would be most effective with a well-defined, well-scoped purpose.
8. The distributed cloud
The distributed cloud pertains to the distribution of open cloud services to areas outside the cloud provider’s physical data hubs but are still regulated by the provider. The cloud provider is liable for all features of cloud service design, delivery, performance, restrictions, and updates. The evolution from a centralized public cloud to a distributed public cloud paves the way for a new period of cloud computing.
Distributed cloud enables data hubs to be established anywhere, solving both technical problems like latency as well as regulatory issues like data independence. Additionally, it offers the advantages of a public cloud service together with the advantages of a private and local cloud.
9. Practical blockchain
Blockchain is an example of a distributed ledger or an expanding chronologically arranged list of cryptographically signed, permanent transactional records used by all members in a network.
This enables parties to trace assets and their origin, a feature that’s useful for traditional assets, but also opens the possibilities for other functions including tracing the original supplier of a food-borne illness.
Blockchain also allows unfamiliar parties to securely connect in a digital setting and transfer value without requiring a centralized authority.
The blockchain model has five elements:
- A shared and distributed ledger.
- Permanent and traceable ledger.
- Distributed public agreement device.
Unfortunately, blockchain is still considered juvenile for business use because of outstanding technical problems like poor scalability and interoperability. On a brighter note, blockchain is foreseen to be fully scalable by 2023.
For now, enterprise blockchains practice a pragmatic approach and execute select elements of a complete blockchain. Everyone with the granted access can see the same information, and integration is made simple by using a single shared blockchain while traditional private models are used for consensus.
With complementary technologies such as AI and the IoT (Internet of Things), true blockchain or “blockchain complete” can potentially transform industries, and eventually, the world’s economy. This opens the kinds of organizations that could include machines, from money to real estate.
Something as crazy as a car being capable of negotiating insurance prices directly with an insurance company using the data it gathered from its sensors can be possible.
10. AI security
Hyperautomation and autonomous devices offer vast business opportunities. However, they also open unique security vulnerabilities and brand-new potential points of attack. So, security teams must be strengthened to approach such attacks and be ready for the impact that AI will create on the security space.
Here are three key factors in AI security:
- AI-powered systems protection. This involves securing AI training data, training pipelines, and ML models.
- Enhance security defense. Using machine learning to follow patterns, uncover breaches, and automate parts of the cybersecurity model.
- Anticipating malicious use of AI. Being able to identify attacks and being ready to defend against them.