In the meantime, the practical consequence of theвЂњno free lunchвЂќ theorem is that thereвЂ™s no such thing as learning without knowledge. Data alone is not enough. Starting from scratch will only get you to scratch. Machine learning is a kind of knowledge pump: we can use it to extract a lot of knowledge from data, but first we have to prime the pump.. One such rule is:If Socrates is human, then heвЂ™s mortal. This does the job, but is not very useful because itвЂ™s specific to Socrates. But now we apply NewtonвЂ™s principle and generalize the rule to all entities:If an entity is human, then itвЂ™s mortal. Or, more succinctly:All humans are mortal. Of course, it would be rash to induce this rule from Socrates alone, but we know similar facts about other humans:. Socrates is a philosopher.. Curing cancer means stopping the bad cells from reproducing without harming the good ones. That requires knowing how they differ, and in particular how their genomes differ, since all else follows from that. Luckily, gene sequencing is becoming routine and affordable. Using it, we can learn to predict which drugs will work against which cancer genes. This contrasts with traditional chemotherapy, which affects all cells indiscriminately. Learning which drugs work against which mutations requires a database of patients, their cancersвЂ™ genomes, the drugs tried, and the outcomes. The simplest rules encode one-to-one correspondences between genes and drugs, such asIf the BCR-ABL gene is present, then use Gleevec. (BCR-ABL causes a type of leukemia, and Gleevec cures it in nine out of ten patients.) Once sequencing cancer genomes and collating treatment outcomes becomes standard practice, many more rules like this will be discovered.. Connectionists, in particular, are highly critical of symbolist learning. According to them, concepts you can define with logical rules are only the tip of the iceberg; thereвЂ™s a lot going on under the surface that formal reasoning just canвЂ™t see, in the same way that most of what goes on in our minds is subconscious. You canвЂ™t just build a disembodied automated scientist and hope heвЂ™ll do something meaningful-you have to first endow him with something like a real brain, connected to real senses, growing up in the world, perhaps even stubbing his toe every now and then. And how do you build such a brain? By reverse engineering the competition. If you want to reverse engineer a car, you look under the hood. If you want to reverse engineer the brain, you look inside the skull.. [РљР°СЂС‚РёРЅРєР°: pic_9.jpg]. A spin glass is still a very unrealistic model of the brain, though. For one, spin interactions are symmetric, and connections between neurons in the brain are not. Another big issue that HopfieldвЂ™s model ignored is that real neurons are statistical: they donвЂ™t deterministically turn on and off as a function of their inputs; rather, as the weighted sum of inputs increases, the neuron becomes more likely to fire, but itвЂ™s not certain that it will. In 1985, David Ackley, Geoff Hinton, and Terry Sejnowski replaced the deterministic neurons in Hopfield networks with probabilistic ones. A neural network now had a probability distribution over its states, with higher-energy states being exponentially less likely than lower-energy ones. In fact, the probability of finding the network in a particular state was given by the well-known Boltzmann distribution from thermodynamics, so they called their network a Boltzmann machine.. [РљР°СЂС‚РёРЅРєР°: pic_17.jpg]. The states form a Markov chain, as before, but we donвЂ™t get to see them; we have to infer them from the observations. This is called a hidden Markov model, or HMM for short. (Slightly misleading, because itвЂ™s the states that are hidden, not the model.) HMMs are at the heart of speech-recognition systems like Siri. In speech recognition, the hidden states are written words, the observations are the sounds spoken to Siri, and the goal is to infer the words from the sounds. The model has two components: the probability of the next word given the current one, as in a Markov chain, and the probability of hearing various sounds given the word being pronounced. (How exactly to do the inference is a fascinating problem that weвЂ™ll turn to after the next section.). [РљР°СЂС‚РёРЅРєР°: pic_20.jpg]. Of course, frequentists are aware of this issue, and their answer is to, for example, multiply the likelihood by a factor that penalizes more complex networks. But at this point frequentism and Bayesianism have become indistinguishable, and whether you call the scoring functionвЂњpenalized likelihoodвЂќ or вЂњposterior probabilityвЂќ is really just a matter of taste.. You rack your brains for a solution, but the more you try, the harder it gets. Perhaps unifying logic and probability is just beyond human ability. Exhausted, you fall asleep. A deep growl jolts you awake. The hydra-headed complexity monster pounces on you, jaws snapping, but you duck at the last moment. Slashing desperately at the monster with the sword of learning, the only one that can slay it, you finally succeed in cutting off all its heads. Before it can grow new ones, you run up the stairs.. Whether you read this book out of curiosity or professional interest, I hope you will share what youвЂ™ve learned with your friends and colleagues. Machine learning touches the lives of every one of us, and itвЂ™s up to all of us to decide what we want to do with it. Armed with your new understanding of machine learning, youвЂ™re in a much better position to think about issues like privacy and data sharing, the future of work, robot warfare, and the promise and peril of AI; and the more of us have this understanding, the more likely weвЂ™ll avoid the pitfalls and find the right paths. ThatвЂ™s the other big reason I wrote this book. The statistician knows that prediction is hard, especiallyabout the future, and the computer scientist knows that the best way to predict the future is to invent it, but the unexamined future is not worth inventing.. The NaГЇve Bayes algorithm is first mentioned inPattern Classification and Scene Analysis,* by Richard Duda and Peter Hart (Wiley, 1973). Milton Friedman argues for oversimplified theories inвЂњThe methodology of positive economics,вЂќ which appears inEssays in Positive Economics (University of Chicago Press, 1966). The use of NaГЇve Bayes in spam filtering is described in вЂњStopping spam,вЂќ by Joshua Goodman, David Heckerman, and Robert Rounthwaite (Scientific American, 2005).вЂњRelevance weighting of search terms,вЂќ* by Stephen Robertson and Karen Sparck Jones (Journal of the American Society for Information Science, 1976), explains the use of NaГЇve Bayes-like methods in information retrieval.. Efficient MLNs with hierarchical class and part structure are described inвЂњLearning and inference in tractable probabilistic knowledge bases,вЂќ* by Mathias Niepert and Pedro Domingos (Proceedings of the Thirty-First Conference on Uncertainty in Artificial Intelligence, 2015). GoogleвЂ™s approach to parallel gradient descent is described in вЂњLarge-scale distributed deep networks,вЂќ* by Jeff Dean et al. (Advances in Neural Information Processing Systems 25, 2012).вЂњA general framework for mining massive data streams,вЂќ* by Pedro Domingos and Geoff Hulten (Journal of Computational and Graphical Statistics, 2003), summarizes our sampling-based method for learning from open-ended data streams. The FuturICT project is the subject ofвЂњThe machine that would predict the future,вЂќ by David Weinberger (Scientific American, 2011)..