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Technology Stocks : Artificial Intelligence, Robotics, Chat bots - ChatGPT -- Ignore unavailable to you. Want to Upgrade?

To: Glenn Petersen who wrote (1355)	10/1/2021 5:50:43 AM
From: Glenn Petersen	2 Recommendations Recommended By Frank Sully Zen Dollar Round Read Replies (1) \| Respond to of 5554

An Inconvenient Truth About AI AI won't surpass human intelligence anytime soon RODNEY BROOKS IEEE Spectrum - Opinion 9 SEP 2021 3 MIN READ WE ARE WELL INTO the third wave of major investment in artificial intelligence. So it's a fine time to take a historical perspective on the current success of AI. In the 1960s, the early AI researchers often breathlessly predicted that human-level intelligent machines were only 10 years away. That form of AI was based on logical reasoning with symbols, and was carried out with what today seem like ludicrously slow digital computers. Those same researchers considered and rejected neural networks. In the 1980s, AI's second age was based on two technologies: rule-based expert systems—a more heuristic form of symbol-based logical reasoning—and a resurgence in neural networks triggered by the emergence of new training algorithms. Again, there were breathless predictions about the end of human dominance in intelligence. The third and current age of AI arose during the early 2000s with new symbolic-reasoning systems based on algorithms capable of solving a class of problems called 3SAT and with another advance called simultaneous localization and mapping. SLAM is a technique for building maps incrementally as a robot moves around in the world. In the early 2010s, this wave gathered powerful new momentum with the rise of neural networks learning from massive data sets. It soon turned into a tsunami of promise, hype, and profitable applications. Regardless of what you might think about AI, the reality is that just about every successful deployment has either one of two expedients: It has a person somewhere in the loop, or the cost of failure, should the system blunder, is very low. In 2002, iRobot, a company that I cofounded, introduced the first mass-market autonomous home-cleaning robot, the Roomba, at a price that severely constricted how much AI we could endow it with. The limited AI wasn't a problem, though. Our worst failure scenarios had the Roomba missing a patch of floor and failing to pick up a dustball. That same year we started deploying the first of thousands of robots in Afghanistan and then Iraq to be used to help troops disable improvised explosive devices. Failures there could kill someone, so there was always a human in the loop giving supervisory commands to the AI systems on the robot. These days AI systems autonomously decide what advertisements to show us on our Web pages. Stupidly chosen ads are not a big deal; in fact they are plentiful. Likewise search engines, also powered by AI, show us a list of choices so that we can skip over their mistakes with just a glance. On dating sites, AI systems choose who we see, but fortunately those sites are not arranging our marriages without us having a say in it. So far the only self-driving systems deployed on production automobiles, no matter what the marketing people may say, are all Level 2. These systems require a human driver to keep their hands on the wheel and to stay attentive at all times so that they can take over immediately if the system is making a mistake. And there have already been fatal consequences when people were not paying attention. Just about every successful deployment of AI has either one of two expedients: It has a person somewhere in the loop, or the cost of failure, should the system blunder, is very low. These haven't been the only terrible failures of AI systems when no person was in the loop. For example, people have been wrongly arrested based on face-recognition technology that works poorly on racial minorities, making mistakes that no attentive human would make. Sometimes we are in the loop even when the consequences of failure aren't dire. AI systems power the speech and language understanding of our smart speakers and the entertainment and navigation systems in our cars. We, the consumers, soon adapt our language to each such AI agent, quickly learning what they can and can't understand, in much the same way as we might with our children and elderly parents. The AI agents are cleverly designed to give us just enough feedback on what they've heard us say without getting too tedious, while letting us know about anything important that may need to be corrected. Here, we, the users, are the people in the loop. The ghost in the machine, if you will. Ask not what your AI system can do for you, but instead what it has tricked you into doing for it. SOURCE: GOOGLE NGRAMS This article appears in the October 2021 print issue as "A Human in the Loop." An Inconvenient Truth About AI - IEEE Spectrum

To: Glenn Petersen who wrote (1355)	10/1/2021 6:33:00 AM
From: Glenn Petersen	1 Recommendation Recommended By Frank Sully Respond to of 5554

How Deep Learning Works Inside the neural networks that power today's AI SAMUEL K. MOORE DAVID SCHNEIDER ELIZA STRICKLAND IEEE Spectrum 28 SEP 2021 3 MIN READ TODAY'S BOOM IN AI is centered around a technique called deep learning, which is powered by artificial neural networks. Here's a graphical explanation of how these neural networks are structured and trained. ARCHITECTURE Each neuron in an artificial neural network sums its inputs and applies an activation function to determine its output. This architecture was inspired by what goes on in the brain, where neurons transmit signals between one another via synapses. DAVID SCHNEIDER ----------------------- Here's the structure of a hypothetical feed-forward deep neural network ("deep" because it contains multiple hidden layers). This example shows a network that interprets images of hand-written digits and classifies them as one of the 10 possible numerals. The input layer contains many neurons, each of which has an activation set to the gray-scale value of one pixel in the image. These input neurons are connected to neurons in the next layer, passing on their activation levels after they have been multiplied by a certain value, called a weight. Each neuron in the second layer sums its many inputs and applies an activation function to determine its output, which is fed forward in the same manner. TRAINING This kind of neural network is trained by calculating the difference between the actual output and the desired output. The mathematical optimization problem here has as many dimensions as there are adjustable parameters in the network—primarily the weights of the connections between neurons, which can be positive [blue lines] or negative [red lines]. Training the network is essentially finding a minimum of this multidimensional "loss" or "cost" function. It's done iteratively over many training runs, incrementally changing the network's state. In practice, that entails making many small adjustments to the network's weights based on the outputs that are computed for a random set of input examples, each time starting with the weights that control the output layer and moving backward through the network. (Only the connections to a single neuron in each layer are shown here, for simplicity.) This backpropagation process is repeated over many random sets of training examples until the loss function is minimized, and the network then provides the best results it can for any new input. DAVID SCHNEIDER STEP 1 When presented with a handwritten "3" at the input, the output neurons of an untrained network will have random activations. The desire is for the output neuron associated with 3 to have high activation [dark shading] and other output neurons to have low activations [light shading]. So the activation of the neuron associated with 3, for example, must be increased [purple arrow]. STEP 2 To do that, the weights of the connections from the neurons in the second hidden layer to the output neuron for the digit "3" should be made more positive [black arrows], with the size of the change being proportional to the activation of the connected hidden neuron. STEP 3 A similar process is then performed for the neurons in the second hidden layer. For example, to make the network more accurate, the top neuron in this layer may need to have its activation reduced [green arrow]. The network can be pushed in that direction by adjusting the weights of its connections with the first hidden layer [black arrows]. STEP 4 The process is then repeated for the first hidden layer. For example, the first neuron in this layer may need to have its activation increased [orange arrow]. How Deep Learning Works - IEEE Spectrum