CoSIT 2017

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62 Computer Science & Information Technology (CS & IT) Figure 2: Predictions from the MIRI dataset surveys. Source [10] 2.6. RoboEthics The academic study of Robot Ethics or “RoboEthics” is a well-established position in educational and research institutes. In part, this is a response to the understanding that robots, in particular drones, are entering the military as lethal weapons of war. And as such need regulation and legal codes of conduct [14]. But, is also concerned about the rights of robots themselves, as anticipated sapient and sentient “beings” with inalienable rights [15]. 3. PITFALLS – RISK OF AI B AI BEING OVERHYPED AND OR DANGEROUS 3.1. Recent failure stories in AI But, not all voices are positive ones over AI. First of all, not all AI developments in the past few years have had positive results. Many no doubt go unreported, but two high profile ones in recent time are Microsoft’s Tay AI Bot (a Machine Learning online chatbot that learns from its interactions). Tay developed a “character” with strong racist, sexist opinions, and had to be removed [16] Furthermore, ongoing problems with AI messaging chatbots, at Google, resulted in a rollback in their adoption [17]. While these, perhaps overly ambitious projects did not meet the
Computer Science & Information Technology (CS & IT) 63 expectations of the management, they nevertheless provide a proving ground for what can be done with today’s technology and what needs further work. Such feedback is helpful in setting more realistic expectations [18]. There are other noted failures in AI projects, e.g., the fatal crash of a Tesla Automated Driving system in 2016, whereby autopilot sensors on the Model S failed to distinguish a white tractortrailer crossing the highway against a bright sky. In this case, it might be claimed that the testing/QA failed to identify this scenario and build sufficient responses to factor it safely. 3.2. Known problems with Deep Learning Much, though not all of the recent success in AI has been due to Machine Learning advancement in so-called Deep Learning based Neural Networks. But, these bottom-up learning based systems (so-called because they develop learned experience from hierarchical analysis of basic data types and their deep correlations) have issues. They can learn well, often in tight domains beyond human learned experience and can outperform a human. But, a human can provide a top-down description, a discursive rationalisation to a, “why did you decide to make that move/act” question with a, “because, I think gaining control of the centre area a useful strategy at this point in the game” question. Whereas. a Deep Learning system cannot. It is not rule-based and cannot easily track its “reasoning”. In a sense it is like an experienced financial markets trader who knows when to buy/sell, not because of the analytics, but because he has the intuitive “feel” built over years of trading. However, intuition is not necessarily a good guide when the market modalities change, as many traders have found out to their ultimate cost. As some critics have said, “it is only real intelligence if you can show it’s working” [19]. In applications that require compliance with regulations and legal constraints, such systems may be considered risky. Furthermore, if such a system were committed to a highly Mission Critical Application, it would be difficult to question, supervise and control. Such intuitive systems, once relied upon, cannot easily be “rolled-back”, human operators become reliant on the system to make all the hard decisions. In the event of failure, total system breakdown may ensue. There is a significant risk here. As AI is woven more deeply into the fabric of everyday life, the tension between human operators and AI has become increasingly salient. There is also a paradox: the same technologies that extend the intellectual powers of humans can displace them as well [20]. 3.3. Mysterian Position – Beyond Computation Some AI critics while recognising useful tools can be built do not believe AGI is likely in the short or medium term outlook of understanding of such things as “consciousness, free-will and self-aware visceral experience (qualia).” While this paper will not address these “Hard Problems of Consciousness” concerns; it is perhaps not necessary to incorporate these functional elements. If they cannot be defined in the case of natural human intelligence, we are not in a position to easily address them in our smart tools and AGI. Maybe AGI can pass Turing Tests, but not have these functions. The better question is perhaps, do we have them or are they illusory? See [21]. 3.4. Issues of functional complexity and computational limits Some AI critics, while not accepting necessarily the problems of intelligence being in principle beyond computational methods, believe it is so complex that our current computational
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Computer Science & Information Tech
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Volume Editors Dhinaharan Nagamalai
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Organization General Chair Nataraja
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Technically Sponsored by Computer S
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Fourth International Conference on
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UNSUPERVISED DETECTION OF VIOLENT C
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INVESTIGATING BINARY STRING ENCODIN
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TOWARDS A MULTI-FEATURE ENABLED APP
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3.1. Architecture Computer Science
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ESTIMATING HANDLING TIME OF SOFTWAR
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AUTHORS Computer Science & Informat
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THE ANNUAL REPORT ALGORITHM: RETRIE
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A STUDY ON THE MOTION CHANGE UNDER
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B) Experiment part Computer Science
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ADAPTIVE AUTOMATA FOR GRAMMAR BASED
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AUTHOR INDEX Abdullah A. Al-Shaher
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