Deep generative models (i.e., generative models implemented as multi-layered neural networks) have recently shown striking successes in producing synthetic outputs that capture the form and structure of real visual scenes via the incorporation of attention-like mechanisms (Hong et al., 2015; Reed et al., 2016). For example, in one state-of-the-art generative model known as DRAW, attention allows the system to build up an image incrementally, attending to one portion of a “mental canvas” at a time (Gregor et al., 2015).
In AI, the pace of recent research has been remarkable. Artificial systems now match human performance in challenging object recognition tasks (Krizhevsky et al., 2012) and outperform expert humans in dynamic, …show more content…
For example, when paused at a choice point, ripples of neural activity in the rat hippocampus resemble those observed during subsequent navigation of the available trajectories (“preplay”), as if the animal were “imagining” each possible alternative (Johnson and Redish, 2007; Ólafsdóttir et al., 2015; Pfeiffer and Foster, 2013). Further, recent work has suggested a similar process during non-spatial planning in humans (Doll et al., 2015; Kurth-Nelson et al., 2016). We have discussed above the ways in which the introduction of mechanisms that replay and learn offline from past experiences can improve the performance of deep RL agents such as DQN.
Machine learning techniques have transformed the analysis of neuroimaging datasets—for example, in the multivariate analysis of fMRI and magnetoencephalographic (MEG) data (Cichy et al., 2014; Çukur et al., 2013; Kriegeskorte and Kievit, 2013)—with promise for expediting connectomic analysis (Glasser et al., 2016), among other techniques. Going further, we believe that building intelligent algorithms has the potential to offer new ideas about the underpinnings of intelligence in the brains of humans and other animals. In particular, psychologists and neuroscientists often have only quite vague notions of the
AI Research Are Instructive in the Current AI Environment." Communications of the ACM, vol. 60, no. 10, Oct. 2017, pp. 27-31. EBSCOhost, doi:10.1145/3132724.
could perform multiple tasks effectively from its possessed knowledge without requiring direct programming. This large task domain is important as with one an A.I. has the ability to excel at multiple tasks, causing its uses to be exponentially vast compared to a standard programmed algorithm (Bostrom). But compared to humans A.I.’s task domain is minuscule. Humans have a massive task domain. Humans can perform a copious amount of tasks even with limited knowledge on a subject. For example “a human who can read Chinese characters would likely understand Chinese speech, know something about Chinese culture and even make good recommendations at Chinese restaurants (LeGassick).” Conversely, it would require several completely distinct A.I.s to perform each task. Researchers still do not fully understand why human brains have such large task domains and are struggling to translate this skill in algorithmic terms. Experts assume A.I. is currently on track to be of human-level intelligence, not just in specific tasks, but all around, by 2040-2050 (Bostrom). Another skill
Artificial intelligence and robotics are growing to become increasingly influential in our society. With new technology always comes new concerns however, with this particular technology, there are much greater concerns than any event in history. Artificial intelligence has presented dangers such as a loss of employment across all job spectrums, an exponentially increased wage gap in which there are a select few who hold the majority of the wealth and the rest are impoverished, and the mere fact that the magnitude of knowledge that would be acquired in incomprehensible to humans. The main reason AI is so threatening is its ability to teach itself to improve, this is known as machine learning. Through all the contrasting arguments on the implications of these new technologies, one common fact can be agreed on by supporters and resisters alike: artificial intelligence in inevitable, it is coming, no one truly knows when and how but the fact remains that, eventually, artificial intelligence will be integrated with society as we know it.
I suppose human brain is the most complex machine that ever existed! With over 7.146 billion models it is also the most ubiquitous. Despite the research and the studies, scientists are still unsure of brain complexity. Scientists still do not understand how the brain works. Regardless of defining the functionality of certain areas of the brain, and by understanding some of the mechanics at the neural chemical level, scientists remain ignorant of how the brain coordinates all its activities and develops language, thought and a sense of self. Thus, will human entirely or exactly understand how the brain cause the hearts to beat, or make people happy, breathe without thinking, fall in love, fear see, dream, learn, remember, taste, feel or smell? How could such a small organ that only weight about 3 pounds and around 15 centimeters long, become so complex and complicated?
Recently, neuroscientists have improved the quality of research through the development of new tools that allow a better understanding of the brain. Methods like Magnetic Resonance Imaging (MRI) offer to the scientists the capability of exploring the brain from a deep perspective combining both a good spatial and time resolution (Nelson, 2008). Furthermore, the MRI images not only allow the study of the anatomy of the brain (i.e. structural images), but also the activation of different areas when a task is being performed (i.e. functional MRI). For example MRI images can be utilized to do comparisons in multiple brains in order to identify structural changes (i.e. gray matter loss) or to explore different patterns of activation when a “reading comprehension” task is carried out while scanning the subjects; the latter is known as functional MRI (fMRI). Subsequently, it is possible to make comparisons between healthy and unhealthy brains to understand the underlying neural mechanism of diseases (Gazzaniga, 2013).
We are fortunate to live in a time when resources are readily available for scientific research and advances in technology are allowing us to learn about the brain at increasingly rapid rates. Organizations such as the US National Institute of Mental Health have been established to transform our understanding and treatment of mental disorders (Callie, 2015). While the establishment of organizations to support research is not new, researchers today are able to study the neural activity of the brains of people diagnosed with different mental health disorders which is much more extensive research than we have been able to do at any point in history.
According to Klein (2010) functional neuroimaging technologies, such as positron emission tomography (PET) and functional magnetic resonance imaging (fMRI), have revolutionized neuroscience, and provided crucial tools that link cognitive psychology and traditional neuroscientific models in the diagnosis and treatment of brain disorders (Klein, 2010; Sabb & Bilder, 2006). Neuroimaging refers to a collection of techniques that allow scientists to investigate the functions of the brain through the detection of metabolic changes caused by the increase in neural activity during a task (Klein, 2010). Similarly, Moran and Zaki (2013) state that functional neuroimaging has become a primary tool in the study of human psychology (Moran & Zaki, 2013).
AI may be divided in half: weak AI & strong AI. Weak AI is a term for a “machine” that can “act as if they were intelligent”, and simulate very limited capabilities (Russell, & Norvig, p.1020). Our current level of technology, and weak Ai expands across numerous platforms, and ranges numerous examples such as AARON, Deep Blue, Ubers Automated Vehicles, and Watson (Weaver, p.7). In contrast, strong AI is the hypothetical “foundation” for an intelligent agent with cognitive abilities similar to that of a human being (Russell & Norvig, pg. 1020). Accordingly, the aspect that separates weak AI from strong AI is the level of algorithmic programming; generally, AI’s are extremely complex machines capable of acting
In On Intelligence, Jeff Hawkins hypothesizes a system of human intelligence based on memory, predictions, and pattern matching in what he calls a “memory-prediction framework.” Because he studied and worked in computer science, rather than neuroscience, Hawkins has a unique perspective and writes in terms that are accessible to someone from a non-neurological background or standpoint and often analogous to computer architecture, making the subject matter relatable to the interested layman. However, though he offers a plausible structure of the brain that mostly aligns with current thinking, he does not take into account important recent research, omitting neurologically developments that have been accepted by the scientific community. The main difference that he offers, between traditional approaches to artificial intelligence and his thinking, is the importance of drawing on memory (and associated learned patterns and processes) versus computation. He discusses at length a hierarchical system of the brain, but leaves out two very important components to that system: the thalamus and hypothalamus. He also draws broad conclusions without discussing specific mechanisms for how they are achieved, so his theory is not entirely sound, though it could prove to be. Also, possibly because of his background, and not being immersed in the world of biological science, he is able to critique areas of neuroscience and artificial intelligence with interesting points about each. The book
For decades, scientists have been wondering how the brain is capable of producing and understanding abstract concepts. What makes the brain of a genius, take Steven Hawking or Albert Einstein, different from any regular person? Can this level of genius be achieved through training, or does it have to do with the way each individual’s brain is wired? Scientists are working to prove that the brain is ultimately wired different depending on the individual. Extensive research is being done, and all of the results point to one overall hypothesis—
Human-like mental agility are being created via artificial intelligence and that even our clothing will have intelligence as a consequence of the development of smaller and more powerful computers! At this rate, very soon, computers with processing power that rivals the human brain with artificial intelligence could be created.
Ferrari, R. (2017, October 27). Large study uncovers genes linked to intelligence. The Conservation. Retrieved from https://theconversation.com
Research in the field of animal intelligence is essential to understand the more complex aspects of human intelligence. Ken Richardson, an honorary senior research fellow in the center of human development and learning at the Open University, in his book