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‎docs/build/html/.buildinfo‎

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# Sphinx build info version 1
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‎docs/build/html/_sources/part1/chap1.rst.txt‎

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1.2.6 Computer engineering
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^^^^^^^^^^^^^^^^^^^^^^^^^^
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* How can we build an efficient computer?
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**Moore’s law** states that performance doubled every 18 months.
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**Quantum computing** holds out the promise of far greater accelerations for
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some important subclasses of AI algorithms.
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1.2.7 Control theory and cybernetics
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^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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* How can artifacts operate under their own control?
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Modern control theory, especially the branch known as stochastic optimal
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control, has as its goal the design of systems that maximize a **cost function**
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over time.
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1.2.8 Linguistics
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^^^^^^^^^^^^^^^^^
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* How does language relate to thought?
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Modern linguistics and AI, then, were “born” at about the same time, and grew up
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together, intersecting in a hybrid field called **computational linguistics** or
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**natural language processing**.
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1.3 The History of Artificial Intelligence
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------------------------------------------
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1.3.1 The inception of artificial intelligence (1943-1956)
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^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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The first work that is now generally recognized as AI was done by Warren
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McCulloch and Walter Pitts (1943).
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1.3.2 Early enthusiasm, great expectations (1952-1969)
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^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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The intellectual establishment of the 1950s, by and large, preferred to believe
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that "a machine can never do X".
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John McCarthy referred to this period as the "Look, Ma, no hands!" era.
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1.3.3 A does of reality (1966-1973)
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^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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In almost all cases these early systems failed on more difficult problems.
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The illusion of unlimited computational power was not confined to problem-solving programs.
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1.3.4 Expert systems (1969-1986)
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^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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The picture of problem solving that had arisen during the first decade of AI
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research was of a general-purpose search mechanism trying to string together
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elementary reasoning steps to find complete solutions.
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Such approaches have been called **weak methods**.
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The alternative to weak methods is to use more powerful, domain-specific
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knowledge that allows larger reasoning steps and can more easily handle
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typically occurring cases in narrow areas of expertise.
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1.3.5 The return of neural networks (1986-present)
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^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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In the mid-1980s at least four different groups reinvented the
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**back-propagation** learning algorithm first developed in the early 1960s.
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1.3.6 Probabilistic reasoning and machine learning (1987-present)
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^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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1.3.7 Big data (2001-present)
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^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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1.3.8 Deep learning (2011-present)
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^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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‎docs/build/html/part1/chap1.html‎

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<li class="toctree-l4"><a class="reference internal" href="#linguistics">1.2.8 Linguistics</a></li>
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</ul>
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</li>
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<li class="toctree-l3"><a class="reference internal" href="#the-history-of-artificial-intelligence">1.3 The History of Artificial Intelligence</a></li>
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<li class="toctree-l3"><a class="reference internal" href="#the-history-of-artificial-intelligence">1.3 The History of Artificial Intelligence</a><ul>
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<li class="toctree-l4"><a class="reference internal" href="#the-inception-of-artificial-intelligence-1943-1956">1.3.1 The inception of artificial intelligence (1943-1956)</a></li>
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<li class="toctree-l4"><a class="reference internal" href="#early-enthusiasm-great-expectations-1952-1969">1.3.2 Early enthusiasm, great expectations (1952-1969)</a></li>
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<li class="toctree-l4"><a class="reference internal" href="#a-does-of-reality-1966-1973">1.3.3 A does of reality (1966-1973)</a></li>
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<li class="toctree-l4"><a class="reference internal" href="#expert-systems-1969-1986">1.3.4 Expert systems (1969-1986)</a></li>
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<li class="toctree-l4"><a class="reference internal" href="#the-return-of-neural-networks-1986-present">1.3.5 The return of neural networks (1986-present)</a></li>
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<li class="toctree-l4"><a class="reference internal" href="#probabilistic-reasoning-and-machine-learning-1987-present">1.3.6 Probabilistic reasoning and machine learning (1987-present)</a></li>
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<li class="toctree-l4"><a class="reference internal" href="#big-data-2001-present">1.3.7 Big data (2001-present)</a></li>
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<li class="toctree-l4"><a class="reference internal" href="#deep-learning-2011-present">1.3.8 Deep learning (2011-present)</a></li>
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</ul>
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</li>
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<li class="toctree-l3"><a class="reference internal" href="#the-sate-of-the-art">1.4 The Sate of the Art</a></li>
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<li class="toctree-l3"><a class="reference internal" href="#risks-and-benefits-of-ai">1.5 Risks and Benefits of AI</a></li>
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</ul>
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</div>
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<div class="section" id="computer-engineering">
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<h3>1.2.6 Computer engineering<a class="headerlink" href="#computer-engineering" title="Permalink to this headline"></a></h3>
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<ul class="simple">
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<li><p>How can we build an efficient computer?</p></li>
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</ul>
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<p><strong>Moore’s law</strong> states that performance doubled every 18 months.
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<strong>Quantum computing</strong> holds out the promise of far greater accelerations for
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some important subclasses of AI algorithms.</p>
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</div>
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<div class="section" id="control-theory-and-cybernetics">
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<h3>1.2.7 Control theory and cybernetics<a class="headerlink" href="#control-theory-and-cybernetics" title="Permalink to this headline"></a></h3>
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<ul class="simple">
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<li><p>How can artifacts operate under their own control?</p></li>
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</ul>
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<p>Modern control theory, especially the branch known as stochastic optimal
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control, has as its goal the design of systems that maximize a <strong>cost function</strong>
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over time.</p>
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</div>
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<div class="section" id="linguistics">
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<h3>1.2.8 Linguistics<a class="headerlink" href="#linguistics" title="Permalink to this headline"></a></h3>
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<ul class="simple">
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<li><p>How does language relate to thought?</p></li>
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</ul>
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<p>Modern linguistics and AI, then, were “born” at about the same time, and grew up
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together, intersecting in a hybrid field called <strong>computational linguistics</strong> or
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<strong>natural language processing</strong>.</p>
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</div>
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</div>
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<div class="section" id="the-history-of-artificial-intelligence">
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<h2>1.3 The History of Artificial Intelligence<a class="headerlink" href="#the-history-of-artificial-intelligence" title="Permalink to this headline"></a></h2>
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<div class="section" id="the-inception-of-artificial-intelligence-1943-1956">
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<h3>1.3.1 The inception of artificial intelligence (1943-1956)<a class="headerlink" href="#the-inception-of-artificial-intelligence-1943-1956" title="Permalink to this headline"></a></h3>
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<p>The first work that is now generally recognized as AI was done by Warren
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McCulloch and Walter Pitts (1943).</p>
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</div>
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<div class="section" id="early-enthusiasm-great-expectations-1952-1969">
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<h3>1.3.2 Early enthusiasm, great expectations (1952-1969)<a class="headerlink" href="#early-enthusiasm-great-expectations-1952-1969" title="Permalink to this headline"></a></h3>
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<p>The intellectual establishment of the 1950s, by and large, preferred to believe
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that “a machine can never do X”.
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John McCarthy referred to this period as the “Look, Ma, no hands!” era.</p>
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</div>
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<div class="section" id="a-does-of-reality-1966-1973">
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<h3>1.3.3 A does of reality (1966-1973)<a class="headerlink" href="#a-does-of-reality-1966-1973" title="Permalink to this headline"></a></h3>
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<p>In almost all cases these early systems failed on more difficult problems.
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The illusion of unlimited computational power was not confined to problem-solving programs.</p>
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</div>
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<div class="section" id="expert-systems-1969-1986">
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<h3>1.3.4 Expert systems (1969-1986)<a class="headerlink" href="#expert-systems-1969-1986" title="Permalink to this headline"></a></h3>
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<p>The picture of problem solving that had arisen during the first decade of AI
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research was of a general-purpose search mechanism trying to string together
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elementary reasoning steps to find complete solutions.
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Such approaches have been called <strong>weak methods</strong>.
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The alternative to weak methods is to use more powerful, domain-specific
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knowledge that allows larger reasoning steps and can more easily handle
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typically occurring cases in narrow areas of expertise.</p>
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</div>
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<div class="section" id="the-return-of-neural-networks-1986-present">
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<h3>1.3.5 The return of neural networks (1986-present)<a class="headerlink" href="#the-return-of-neural-networks-1986-present" title="Permalink to this headline"></a></h3>
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<p>In the mid-1980s at least four different groups reinvented the
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<strong>back-propagation</strong> learning algorithm first developed in the early 1960s.</p>
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</div>
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<div class="section" id="probabilistic-reasoning-and-machine-learning-1987-present">
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<h3>1.3.6 Probabilistic reasoning and machine learning (1987-present)<a class="headerlink" href="#probabilistic-reasoning-and-machine-learning-1987-present" title="Permalink to this headline"></a></h3>
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</div>
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<div class="section" id="big-data-2001-present">
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<h3>1.3.7 Big data (2001-present)<a class="headerlink" href="#big-data-2001-present" title="Permalink to this headline"></a></h3>
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</div>
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<div class="section" id="deep-learning-2011-present">
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<h3>1.3.8 Deep learning (2011-present)<a class="headerlink" href="#deep-learning-2011-present" title="Permalink to this headline"></a></h3>
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</div>
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</div>
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<div class="section" id="the-sate-of-the-art">
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<h2>1.4 The Sate of the Art<a class="headerlink" href="#the-sate-of-the-art" title="Permalink to this headline"></a></h2>

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