The amount of data produced and stored over the years has been increasing exponentially. To handle the deluge of data, there has been a massive call for data driven decision-making, proof of evidence, and analytic solution deployment.
This has resulted in massive, industry-changing disruption. Below we will explore the methods, results, and future trends of this change.
Capacity for Data Integration
When observing human experts solve problems we see a process of:
- Guided information gathering
- Reasoning about possible influences
- Continuous updates of their initial hypotheses in a feedback loop as new information is incorporated
To measure the impact on their ability to make decisions they look at:
- The amount and quality of the available data
- The knowledge of the context they are working in
- The information harvesting processes used to reach conclusions from the data
Human experts capable of accurately making complex decisions are scarce, and therefore expensive. This is due to the need of years of training/education required to gain a deep understanding of their field. Additionally, in the process of becoming an expert, there are always a number of costly errors.
When we make daily decisions on subjects about which we lack expert knowledge, we naturally wish we could be more informed – without having to go through a painful learning process. This desire gave rise to the creation of expert and knowledge-based systems. These attempt to support or even automate an expert’s method of deriving information from data in order to come to an actionable conclusion.
Dimensionality of Decision Making
Humans have a working memory capable of storing between 5 +/-2 pieces of information, and store long-term memory via an estimated 100 trillion neural connections. As we discover more details about how we encode information in our brain, modern A.I. based on deep learning has opened up ways to automate many ways of deriving information from data that used to require extensive effort by human experts.
While older A.I. systems failed to scale because of the human work involved, deep-learning based machine systems now glean information on a wide scale from massive datasets, in many cases without any human supervision.
The Future of Work
In a recent world-wide Dell study, 45% of 4,000 senior decision makers said they were concerned with becoming obsolete in just 3-5 years.
Nearly half had no insight on how their own industry would even look in about three years. The advent of advanced emerging technologies such as A.I. (Artificial Intelligence), A.R. (Augmented Reality), home robotics, IoT (smart devices/Internet of Things) have many believing that within ten years, we will witness a disruptive change in almost all walks of professional and personal life.
With so many amazing and wonderful prospects, the most immediate applications of A.I. to support humans in expert work currently fall into three key areas:
- Helping augment decision making and human creativity, especially by digesting large sets of available data
- Optimizing systems for tackling industry specific, deep domain problems
- Developing ethical and explainable A.I., taking accountability and responsibility into consideration
Imagine learning a new musical instrument by listening to, and then attempting to play a song. You would note if it sounds close enough to the original tune and make appropriate adjustments. In this way, all cognitive systems are learning from ongoing feedback.
The more efficient we are in the evaluation of our actions, the faster we learn and the more responsive we are to change. This applies directly to business as well as individuals, as those who quickly collect and act on feedback respond sooner, capitalizing on opportunities before their competition.
Up until now, we have focused on providing tools to augment our naked human senses to improve our ability to discern information that was previously unknowable. We have built mechanisms to provide power and scale to our actions and automate repetitive processes beyond our human limbs. We have built computers to digitize, catalog, and store information, and facilitate all of the above.
The next frontier will involve the augmentation of the human mind, both in our capacity to interact with data, but also in our capability to understand and reason with that information. From non-invasive methods of recording thoughts and directives allowing us to interface with machines without tradition mechanical inputs such as keyboards and mice, to efforts enabling us to read information directly with our thoughts, we are working towards “augmented intelligence”. This is obviously some time off, but the work is well underway.
Collaboration Between Humans and Machines
How will these developments influence the type of work we will do in the near future, and what are the first use cases we are already seeing? Humans are able to reach decisions even under uncertain conditions with imperfect knowledge (the “educated guess”). Despite this, A.I. approaches have been widely proven to be far more statistically accurate. This is a big disadvantage for humans when dealing with large-scale data, something that is becoming necessary for everyday business.
Enabling Human Experts to “Scale Themselves”
As skills become commoditized into “building blocks” able to be sold and purchased in the nascent “skill economy”, human experts free themselves from traditional employment. We witness this in the tech industry: From remote team-based work, the “digital natives” with a distributed and flexible lifestyle (and global impact), to the $1.5 trillion-dollar freelance “gig” economy.
This is also now the case for many other industries. Where programming was specific only to computers and electronics (as well as advanced analytics), we are now witnessing a global shift towards “data literacy”. Simultaneously, the advances of modern A.I. continue to push labor automation toward more cognitive tasks.
A.I. is even beginning to be felt in creative fields. It is starting to create or co-create art, music, and even written works and product design. This is being driven by A.I. models called GANs (Generative Adversarial Networks), which are able to learn and imitate complex patterns.
Scaling the Human Expert
DREAM is providing the world’s leading experts with the tools and the platform to scale their experience and knowledge to a global audience. An expert can use our solution to provide the following professional services:
- Provide access to domain-specific data archives. Provide the necessary domain knowledge and information for human experts and A.I. bots to use on the platform during decision making. Access to the data and the amount of used information are awarded to the expert.
- Train and teach A.I. agents to take complex decisions and apply them automatically as a professional consulting service. The human expert observes and analyzes a specific process of consulting, and provides the necessary domain expertise and decision processes to the system to automate the service for a large group of users. Whenever the solution is used by another user, the expert gets rewarded for their agent’s work.
- Offer A.I. agent-based services that fall into domain-specific tasks, e.g. logo design, article writing for social media content, programming small application parts, and other product deliverables. Here the expert trains the agent, which offers the products as automatically generated content on the platform. Users can purchase them as need be.
Enabling the Future of Collaborative Work at DREAM
As the world leader in finding freelance blockchain talent we are at the forefront of these trends. We work in a field where the supply of experts is vastly outnumbered by the growing demand for talent to implement this new technology.
We are delivering a web-based platform that supports entrepreneurs and project managers so they can quickly scope and deliver high-end technical projects with the aid of decentralized teams drawn from a global talent pool. This requires providing relevant guidance in understanding how to break down a technical problem in order to implement a solution, and also enables our clients to make sense of the best available data while doing so.
When thinking through a project so it can be successfully brought to market, you need data on: The market/business, existing solutions and how they are implemented, growth rates, and projected market value. In addition to the technical requirements and work necessary to implement a solution, you also need the availability of talent able to deliver the work.
Via a networked world with numerous ratings sites and social media, there are streams of product/brands/technology reviews and mentions, and a constant flow of news feed chatter about events influencing any given market. All this information is currently utilized by large and technically apt organizations, but it remains difficult to integrate this into normal decision processes by smaller/non-tech savvy companies.
This is why we built DREAM Knowledge – An A.I. based platform that enables communities of human experts to build data applications as extensions to our platform, enabling our system to access data sources, extract relevant information in near real-time, and integrate varied information sources into complex decision processes.
Through our voice and text-based interfaces, clients can express their need for specific solutions and have direct access not only to human experts through our talent pool, but also our A.I. bots trained in helping them achieve their goals. DREAM provides a platform and data architecture that not only manages the training of our A.I. agents, but also integrates all relevant data in near real-time.
The DREAM platform isn’t just another untested beta program on a white paper… It’s live and being used right now to hire blockchain professionals. The token sale will enable DREAM’s innovative team take DREAM to the next level by integrating A.I. and incorporating our platform token.
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