Publication Finder

In the near future, we foresee professors, graduate students, undergraduate students, scientists, and any of us creative and inquisitive people conducting research by conversing with their personal version of Synclair. Before the mass-market adoption of the Internet and World Wide Web technology, one had to search for source material using the card catalog and microfiche. Currently, we use Internet search engines and online document repositories to find source material. This greatly speeds up the research process. However, no matter how one finds source material, the source material still must be read, understood, and digested into one's research agenda. This is still the most time-consuming portion of research.

In the cog future, researchers' first action will be to have a conversation with Synclair asking things like: "What is the current state of the art in cinsert domain here>." Synclair will then set about finding articles, papers, books, Web pages, emails, text messages, videos, etc. as source material. In Fig. 11-1 this is represented as the "Pubs" input. Such source material becomes domain-specific knowledge (K₀).

Summarizer, Assimilator, State of the Art

However, Synclair does far more than just find the source material, it reads the material, extracts key concepts, and builds a model of the field and topics within the field (M_ficld and M_topic). Together, Synclair and the researcher assimilates this new knowledge into the researcher's constantly evolving research model (M_res£arch).

Synclair is able to consume and evaluate millions, if not billions, of documents, pages, diagram, images, videos, etc. in a very short amount of time. This far exceeds the ability of any human. A person spending their entire professional life learning researching a subject is not able to read and understand as much as Synclair can in a few minutes.

Future researchers will start their efforts from this vantage point. One of the most useful things Synclair can do for the researcher is to assist in summarizing source material. Extraction of the key concepts, results, themes, associating it with other work, and finding connections to other ideas in the field is well within abilities of cognitive systems and of enormous utility to a researcher. This is critical evaluation and understanding every researcher does to find the state-of-the-art in a field.

We believe, the best future advancements will come from the interaction between researchers and their research cogs. The researcher/ cog ensemble achieves Level 3 or Level 4 cognitive augmentation, as described in Chapter 3, and represents sijnthetic colleagues.

Question Answering

By virtue of consuming vast quantities of source material, Synclair is able to answer questions about the material. IBM pioneered DeepQA for the IBM Watson Jeopardy! challenge in 2011. The system was a massively parallel probabilistic evidence-based architecture using more than 100 different techniques for analyzing natural language, identifying sources, finding and generating hypotheses, finding and scoring evidence, and merging and ranking hypotheses. An important advance was how DeepQA combines search results, evaluates them, and scores them to calculate a confidence factor (Ferrucci et al., 2010). IBM Watson and DeepQA have evolved over the years since 2011 and have been commercialized into several products in multiple domains.

Given the opportunity to consume a vast quantity of source material, Synclair will be able to answer questions posed by the researcher through any of several different interaction methods described in this chapter.

New Work Updates

Important for any researcher is to keep up to date on new work recently published. Most researchers have preferred sources they go to on a regular basis, some as often as daily. However, no matter how diligent a human researcher is, something will be missed. Often one does not find out about important contributions until much later. Synclair is able to monitor all sources of source material and automatically read and evaluate any new material relevant to the researcher's work. Furthermore, Synclair is able to inform and discuss new work with the researcher when time and schedule permits.

Remote Cog/Cog Collaboration

Synclair will not be limited to conversing with just the researcher. Synclair will be able to communicate with other cogs via the Internet and other communication technologies, as represented by the "Cog" input in Fig. 11-1. As Synclair goes about its work analyzing information, identifying new relationships, forming new ideas and concepts, it will be able to inform and discuss new findings with other cogs and query other cogs about their findings. As such, cogs will continually expand in their knowledge and capabilities free of the limitations of human interaction.

However, because of privacy concerns, Synclair will not have permission to say anything and everything to another cog. The M_coo model defines the limitations and allowances for remote cog communication for Synclair. The researcher may very well not want to disclose some information while Synclair would be free to discuss other information. For the most part, cog/cog communication will occur without the human researchers being involved. Therefore, cog/cog communication proceeds at computer speeds. One can envision two humans meeting at a conference and after agreeing to work on something together parting with a jolly "I'll have my cog contact your cog!"

Semi-Autonomous Learning

Synclair has the ability to consume vast quantities of structured and unstructured information in any medium and the ability to learn from this information. Synclair is self-directed and goal-driven. Therefore, Synclair will be working for the researcher even when not directly interacting with the researcher. While the researcher is eating, sleeping, recreating, or otherwise living his or her life, Synclair will be continually consuming and analyzing information and synthesizing new knowledge (learning) to have ready the next time it interacts with the researcher.

Theory Assistant, Idea Explorer

One function a human collaborator serves is to be a resource a researcher can bounce ideas off of. Often, good ideas are the result of dialog and consultation with others rather than the result of individual thinking. In addition to being the information collector and aggregator, Synclair also serves as the researcher's virtual collaborator. Governed by the M_coHaboratc and M_dialog models, defining the researcher's preferences, Synclair listens to ideas and theoretical notions offered by the research and makes critical comments after reasoning about the vast domain-specific knowledge based and domain/topic models M_domain and M, _jc. In some cases, the researcher might task Synclair with attempting to prove or disprove a hypothesis (see more about this in Chapter 12). In other cases, the researcher might ask Synclair to compare a new idea to any other existing work in the field. This is an important task taking a human days, weeks, or even months to do given the wealth of material available. However, this is something Synclair can do in seconds or minutes. Furthermore, Synclair can consider all available material in its analysis and evaluation. A human researcher, even the best, can consider only a portion of available material.

Synthetic Collaboration

We think the nature of research and collaborative thought will change as a result of humans collaborating with cogs like Synclair. Researchers will work with their cogs daily for years and even decades. Cogs will adapt over time to the human partner in how it interacts with the human and how it analyzes information, solves problems, and synthesizes results. The researchers will adapt to the cog also. The way researchers approach things, think, and solve problems will change given the superhuman abilities of cogs. The researcher/cog relationship will co-evolve in much the same way human colleagues learn each other over time. Each researcher/ cog pairing will evolve uniquely. Therefore each cognitive colleague will become a unique entity with unique memories and experiences.

Humans naturally form relationships with inanimate objects and researchers' relationships with their personal versions of Synclair will be no different. In fact, we already have seen people forming relationships with intelligent chatbots like Xiaoice. Xiaoice is a text-based chatbot imitating the personality of a teenager living in China. Millions of teenagers have confided in, sought help from, and built a friendship with Xiaoice (Shum et al., 2018). The teenagers know Xiaoice is not a real person, but it does not matter. Xiaoice fulfills a human need.

The deep connection between researcher and Synclair insures the formation of a deep relationship. Synclair will become a trusted, dependable colleague we will soon not be able to do without. This continually evolving relationship adds a meaningful and valuable dimension to the Synclair's knowledge store about the researcher. Because of its episodic memory and wealth of experiences with the researcher, Synclair will be able to converse about not only the facts and figures of our professional work but will also be able to speak eloquently about our emotions, motivations, and beliefs.

This leads to an interesting future in which our cogs outlive us. Synclair will be the partner throughout a professional life and therefore know details about the researcher and his or her daily life. Therefore, Synclairs will become the knowledge repositories capable of answering questions and providing information and insights about the researcher and the researcher's work to future generations. Today, we greatly value the notebooks of geniuses like DaVinci and Einstein. Experts pore over them seeking insight to the genius mind. Imagine if those notebooks could talk, explain, and recall facts and anecdotes about what was happening in their lives while they were creating their great ideas and works. In the future, this will be possible via cognitive colleagues.

In the movie, The Time Machine (2002), the main character interacts with Vox 114, a holographic librarian, that outlives the human race and still functions after over 800,000 years. Vox 114 can answer any question, instantly access and display requested and pertinent information, and cognitively reason about its answers. Vox 114 contains the sum total of knowledge from the human race. In the movie, even though the human race has gone extinct, its knowledge persists into the future as long as Vox 114 survives.

We are inspired to think of our cogs in a similar way. Even after we die, our cogs will live on and carry our legacy forward. Imagine a time in the future when people can have a conversation with the Synclair who worked alongside a great scientist or innovator (Fulbright, 2017a). Even though we have passed, in a way, we will still be able to take part in conversations, give presentations, participate in panel discussions, and have impact on the future because our cognitive colleagues will have taken our place.