Click to learn more about author Alejandro Correa Bahnsen. Almost everyone has heard the words "Machine Learning", but most people don't fully understand what they mean. Machine Learning isn't a single formula that is simply applied to a problem. There are many algorithms to choose from, each of which can be used to achieve different goals. This is the first in a series of articles that will introduce Machine Learning algorithms to help you understand how they work, and when to use each one.
That was some baseball game last night between the Houston Astros and the LA Dodgers at Minute Maid Park in downtown Houston. The Astros go down 4-0 early, then come back and tie the game, and then the score seesaws back and forth all the way through the bottom of the tenth before Houston's Bregman hit a walk-off single to drive in the winning run. No AI-driven computer simulation could likely have foreseen such an insane game with that outcome. Then again, we might out imagine what would might become of us if it could. Which is why the Information Technology Industry Council, whose members include IBM, Amazon, Facebook, and Oracle, have listed five areas to improve the development of artificial intelligence.
Our overall winner in the category was, perhaps unsurprisingly, Google Assistant. It answered more questions correctly than either Siri or Alexa, as well as generally giving context and often citing a source website for the information. Given that it's backed by Google's powerful search technology, that's to be expected. It fell down only on a couple of questions: It couldn't tell me when the next episode of Arrow aired (though it could interpret that as a TV listing); it gave me departure time for an upcoming flight even though I asked for the arrival time; and in a question about the American League Championship Series, it gave me recent scores, but not the overall standing of the series. However, it was the only one that could tell me how long chicken stays good in the fridge; gave me detailed information about the distance to Jupiter; and correctly identified what most scholars believe to be Shakespeare's first play.
Gary Matthews Jr., the retired professional baseball player who spent three seasons with the Angels, has sold a home in Corona del Mar for $3.69 million. The shake-sided home, built in 1961 and extensively updated, returned to market earlier this year for $3.995 million. Matthews Jr. bought the property a decade ago for $3.05 million, records show. Surrounded by walls and gates, the single-story house is entered through a front courtyard with a swimming pool, a stone fireplace and a separate spa. The pool and spa each have a waterfall feature.
Whether the Dodgers' playoff run ends with a World Series championship or an NLCS loss to the Chicago Cubs, right fielder Yasiel Puig will come home a winner. The slugging outfielder has bought a remodeled Encino estate for $2.65 million, public records show. That's about $350,000 less than what it listed for when it hit the market in June. Set behind a black iron gate, the home is approached by a long red driveway. A tiled entry gives way to hardwood in the living spaces, where expansive windows bring in natural light.
I don't know about you, but I was not the most athletic kid growing up. It took me forever to make a jump shot well. When I started playing golf after college my short game was an absolute disaster. I always had a hard time visualising what I needed to do differently. Having a coach tell me what to do never seemed to do the trick.
Many may think that artificial intelligence will eventually make human capabilities obsolete, but CNBC's Jim Cramer wanted to refine that theory. But that meant that the average amount Williams got it wrong was .600, Cramer said, a dismal statistic anywhere but in a competitive sport. "What would happen if we had artificially intelligent baseball players?" Artificial intelligence and big data will soon let companies to bat a thousand, Cramer said.
A seven-year-old Las Vegas girl will throw out the first pitch in game four of the upcoming World Series. Hailey Dawson was born with Poland syndrome and is missing three fingers on her right hand. At the time, Dawson couldn't find any companies that could fit Hailey with a robotic hand for a reasonable cost. Over more than a year, the UNLV engineering students and faculty worked to develop a variety of robotic 3-D printed hands for then-five-year-old Hailey.
For data prone to noise and anomalies (most data, if we're being honest), a Long Short Term Memory network (LSTM), preserves the long term memory capabilities of the RNN, while filtering out irrelevant data points that are not part of the pattern. Mechanically speaking, the LSTM adds an extra operation to nodes on the map, the outcome of which determines whether the data point will be remembered as part of a potential pattern, used to update the weight matrix, or forgotten and cast aside as noise. For example, to train the HR network, the first input to the network is the number of homers the player hit in his first game, the second input to the network is the number the player hit in his second game and so on. With a network to train and data to train it with, we can now look at a test case where the network attempted to learn Manny Machado's performance patterns and then made some predictions.