GALLE, Sri Lanka (CMC):West Indies captain, Jason Holder, has blamed sloppy fielding by his side for their innings defeat to Sri Lanka in their first cricket Test, which ended at the Galle International Stadium here yesterday.Holder’s men crashed to an innings and six-run defeat to Sri Lanka in the second session of the fourth day of the Test match.The West Indies effort was marred by several sloppy misfields, two missed run-out attempts and five shelled catches, including a sixth that was not attempted.”We created chances with the ball, but we just didn’t take them when we came,” lamented Holder.”If we had, their first innings total would have looked a lot different. If we had new batsmen in at crucial stages when our fast bowlers were fresh, we would have had a chance to have a good burst at them.”Jermaine Blackwood, in a gallant attempt to avert an innings defeat, fell eight runs shy of a century as West Indies were bowled out for 227 in their second innings.Apart from Blackwood the only other West Indian batsman to score a half-century in the match was Darren Bravo with an even 50 in the first innings.Several frontline batsmen again failed to capitalise on starts as the left arm spinner, Rangana Herath, took four second-innings wickets to finish the match with his fifth 10-wicket haul in Tests.”It’s important that we put up a first innings total that’s respectable,” said Holder, who played his first match as Test captain, in just his ninth Test overall.”We only scored 250-odd in the first innings and it set us back. We were pretty much behind the game from there. It was a track you could score runs on once you applied yourselves. Not much in it for the bowlers. Even Rangana Herath – I thought with the harder ball, he was more effective.”Devendra Bishoo grabbed four for 143 as Sri Lanka piled up a huge first-innings score of 484 all out. The 23-year-old skipper applauded the efforts of his bowlers.”I must commend our bowlers. They did really well in the first innings. To come back at the opposition and actually bowl them out was very, very commendable,” Holder said.”I thought our spinners bowled reasonably well and our part-timers in Marlon Samuels and Kraigg Brathwaite did a wonderful job for us. I thought the combination was good enough for this game. It was unfortunate that we didn’t hold our chances. I guess we paid for it.”The second Test will be played in Colombo starting next week Thursday.
Knowledge is the foundation of intelligence— whether artificial intelligence or conventional human intellect. The understanding implicit in intelligence, its application towards business problems or personal ones, requires knowledge of these problems (and potential solutions) to effectively overcome them.The knowledge underpinning AI has traditionally come from two distinct methods: statistical reasoning, or machine learning, and symbolic reasoning based on rules and logic. The former approach learns by correlating inputs with outputs for increasingly progressive pattern identification; the latter approach uses expert, human-crafted rules to apply to particular real-world domains.RELATED CONTENT: Ethical design — What is it and why developers should careTrue or practical AI relies on both approaches. They supplement one another for increasingly higher intelligence and performance levels. Enterprise knowledge graphs— domain knowledge repositories containing ideal machine learning training data—furnish the knowledge base for maximum productivity of total AI. Symbolic reasoningKnowledge graphs are at the basis of symbolic reasoning systems using expert rules for real-life business problems. Regardless of the particular domain, data source, data format, or use case, they seamlessly align data of any variation according to uniform standards focused on relationships between nodes. Semantic rules and inferencing create new types of understanding about business knowledge that machine learning couldn’t generate at all. Examples include optimizing the array of sensor data found in smart cities for event planning based on factors such as traffic patterns, weather conditions, previous event outcomes, and preferences of the hosts and their constituencies. Symbolic reasoning also has the advantage that in the end, one still can explain how and why certain new knowledge and new suggestions were generated.Statistical feedback loopDespite what has been said in the previous paragraphs, knowledge graphs can greatly benefit from machine learning and add value to the symbolic rules-based systems. When modeling car driving behavior, for example, modern image recognition systems (relying on deep learning) can produce more realistic models when deployed in conjunction with rules. Nonetheless, the general paradigm by which machine learning complements rules-based AI is by creating a feedback mechanism for improving the latter’s outcomes—and enhancing the knowledge of semantic graphs.In the preceding smart city use case organizations can deploy machine learning to the outcome of rules-based systems, especially when those outcomes are measured in terms of KPIs. These metrics can assess, for example, the success of the event as measured by the enjoyment of the attendees, the subjective costs and the real costs to the municipality, these costs for the organizations involved in the event, the rate of attendance, etc. Machine learning algorithms can analyze those KPIs for predictions to improve future events.Horizontal applicabilityThe interplay of the knowledge graph foundation with both the statistical and symbolic reasoning form of AI is critical for several reasons. Firstly, they all augment each other. The graphs provide the knowledge for rules-based systems and optimize machine learning training data. The machine learning feedback mechanism improves the graph’s knowledge and the rules, while the output of rules-based systems provides knowledge upon which to run machine learning. Secondly, this process is applicable to any number of horizontal use cases across industries. Most of all, however, there are amazingly advanced applications of AI empowered by this combination, the likes of which makes simple automation seem mundane.There’s risk management use cases in law enforcement and national security in which one can observe terrorists, for example, integrate that information and create hypothetical events or scenarios based on probability (determined by machine learning). Rules-based systems for security measures, then, are transformed into probabilistic rules-based systems that unveil the likelihood of events occurring and how best to mitigate them. Similar processes apply to many other instances of risk management.