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The Potential “Holy Shit” Threats Surrounding AI and ML

first_imgRelated Posts Artificial intelligence(AI) and machine learning (ML) are the most viral topics discussed in this age. It has been a big controversy among scientists today, and their benefits to humankind cannot be overemphasized. We need to watch for and understand the potential “holy shit” threats surrounding AI and ML.Who could have imagined that one day the intelligence of machine would exceed that of a human — a moment futurists call the singularity? Well, a renowned scientist (the forerunner of AI), Alan Turing, proposed in 1950 —  that a machine can be taught just like a child. Turing asked the question, “Can machines think?” Turing also explores the answers to this question and others in one of his most read thesis titled — ‘’Computing Machinery and Intelligence.” In 1955, John McCarthy invented a programming language LISP termed “artificial intelligence.” A few years later, researchers and scientists began to use computers to code, to recognize images, and to translate languages, etc. Even back in 1955 people were hoping that they’d one day make computer to speak and think.Great researchers like Hans Moravec (roboticist), Vernor Vinge (sci-fi author), and Ray Kurzweil were thinking in a broader sense. These men were considering when a machine will become capable of devising ways of achieving its goals all alone. Greats like Stephen Hawking warns that when people become unable to compete with advanced AI, “it could spell the end of the human race.”  “I would say that one of the things we ought not to do is to press full steam ahead on building superintelligence without giving thought to the potential risks. It just feels a bit daft,” said Stuart J. Russell, a professor of computer science at the University of California, Berkeley.Here are five possible dangers of implementing ML and AI and how to fix it:1.  Machine learning (ML) models can be biased — since its in the human nature.As promising as machine learning and AI technology is, its model can also be vulnerable to unintended biases. Yes, some people have the perception that ML models are unbiased when it comes to decision making. Well, they are not wrong, but they happen to forget that humans are teaching these machines —  and by nature — we aren’t perfect.Additionally, ML models can also be biased in decision-making as it wades through data. You know that feeling-biased data (incomplete data), down to the self-learning robot. Can a machine lead to a dangerous outcome? Let’s take for instance, you run a wholesale store, and you want to build a model that will understand your customers. So you build a model that is less likely to default on the purchasing power of your distinguish goods. You also have the hope of using the results of your model to reward your customer at the end of the year.So, you gather your customers buying records — those with a long history of good credit scores, and then developed a model.What if a quota of your most trusted buyers happen to run into debt with banks — and they’re unable to find their feet on time? Of course, their purchasing power will plummet; so, what happens to your model? Certainly it won’t be able to predict the unforeseen rate at which your customers will default. Technically, if you then decide to work with its output result at year end, you’ll be working with biased data.Note: Data is a susceptible element when it comes to machine learning, and to overcome data bias — hire experts that will carefully manage this data for you. Also note that no one but you was looking for this data — but now your unsuspecting customer has a record — and you are holding the “smoking gun” so to speak.  These experts should be ready to honestly question whatever notion that exists in the data accumulation processes; and since this a delicate process, they should also be willing to actively look for ways of how those biases might manifest themselves in data. But look what type of data and record you have created.2. Fixed model pattern.In cognitive technology, this is one of the risks that shouldn’t be ignored when developing a model. Unfortunately, most of the developed models, especially those designed for investment strategy, are the victim of this risk.Imagine spending several months developing a model for your investment. After several trials, you still got an “accurate output.” When you try your model with “real world inputs” (data), it gives you a worthless result.Why is it so? This is because the model lacks variability. This model is built using a specific set of data. It only works perfectly with the data with which it was designed.For this reason, safety conscious AI and ML developers should learn to manage this risk while developing any algorithmic models in the future. By inputting all forms of data variability that they can find, e.g., demo-graphical data sets [yet, that is not all the data.]3. Erroneous interpretation of output data could be a barrier.Erroneous interpretation of data output is another risk machine learning might face in the future. Imagine after you’ve worked so hard to achieve good data, you then do everything right to develop a machine. You decided to share your output result with another party — perhaps your boss for review. After everything — your boss’ interpretation is not even close to your own view. He has a different thought process — and therefore a different bias than you do. You feel lousy thinking how much effort you gave for the success. This scenario happens all the time. That’s why every data scientist should not just be useful in building modeling, but also in understanding and correctly interpreting “every bit” of output result from any designed model. In machine learning, there’s no room for mistakes and assumptions — it just has to be as perfect as possible. If we don’t consider every single angle and possibility, we risk this technology harming humankind.Note: Misinterpretation of any information released from the machine could spell doom for the company. Therefore, data scientists, researchers, and whoever involved shouldn’t be ignorant of this aspect. Their intentions towards developing a machine learning model should be positive, not the other way round.4. AI and ML are still not wholly understood by science.In a real sense, many scientists are still trying to understand what AI and ML are all about fully. While both are still finding their feet in the emerging market, many researchers and data scientists are still digging to know more.With this inconclusive understanding of AI and ML, many people are still scared because they believe that there are still some unknown risks yet to be known. Even big tech companies like Google, Microsoft are still not perfect yet. Tay Ai, an artificial intelligent ChatterBot, was released on the 23 March 2016, by Microsoft Corporation. It was released through twitter to interact with Twitter users — but unfortunately, it was deemed to be a racist. It was shut down within 24 hours.Facebook also found that their chatbots deviated from the original script and started to communicate in a new language it created itself. Interestingly, humans can’t understand this newly created language. Weird, right? Still not fixed — read the fine print. Note: To solve this “existential threat,” scientists and researchers need to understand what AI and ML are. Also, they must also test, test, and test the effectiveness of the machine operational mode before it’s officially released to the public.5. It’s a manipulative immortal dictator.A machine continues forever — and that’s another potential danger that shouldn’t be ignored. AI and ML robots cannot die like a human being. They’re immortal. Once they’re trained to do some tasks, they continue to perform and often without oversight.If artificial intelligence and machine learning properties are not adequately managed or monitored — they can develop into an independent killer machine. Of course, this technology might be beneficial to the military — but what will happen to the innocent citizens if the robot cannot differentiate between enemies and innocent citizens?This model of machines is very manipulative. They learn our fears, dislike and likes, and can use this data against us. Note: AI creators must be ready to take full responsibility by making sure that this risk is considered while designing any algorithmic model.Conclusion: Machine learning is no doubt one of the world most technical capabilities with promising real-world business value — especially when merged with big data technology.  As promising it might look — we shouldn’t neglect the fact that it requires careful planning to suitably avoid the above potential threats: data biases, fixed model pattern, erroneous interpretation, uncertainties, and manipulative immortal dictator. Entrepreneur and Online Marketing Consultant, Ejiofor Francis is the Founder of EffectiveMarketingIdeas. He’s highly enthusiastic about all things business, IT and blockchain technology, and he shares informative resources to help businesses and consumers stay informed, safer, and smarter online. Want to say hi? Shoot him an email at francis@effectivemarketingideas.com What it Takes to Build a Highly Secure FinTech … How Data Analytics Can Save Lives AI: How it’s Impacting Surveillance Data Storage Ejiofor FrancisDigital Marketer, Blogger, IT/Technology Copywriter Leveraging Big Data that Data Websites Should T… Tags:#AI#AI Machine Learning#AI risks#data science#design models last_img read more

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Woman accuses BJP MLA of rape in U.P.

first_imgThe Uttar Pradesh police have initiated an investigation against a BJP MLA from Unnao after a woman from the district accused him of gang-raping her along with his accomplices last June.The case came to light after the woman, along with her family members, threatened to commit suicide outside the residence of Uttar Pradesh Chief Minister Yogi Adityanath on Sunday, alleging that the police refused to take action against the MLA despite her complaints and applications.According to the victim, Kuldeep Singh Sengar, Bangermau MLA, raped her on June 17, 2017, and all her attempts to get justice bore no fruit as his influence prevented police from acting against him. She said that despite her efforts, the police did not register a case against the legislator.Faces threat“When I protested against the rape, he threatened me that he would get my family members killed,” the woman told local television channels.Rajiv Krishnan, Additonal Director-General of Police, Lucknow, said both the allegations made by the woman were of a “serious nature”, and so the case was being shifted from Unnao to Lucknow for a fair investigation.Inspector General, Lucknow Range, would carry out the investigation.Mr. Krishnan said that the woman had alleged that her father was thrashed by people sent by the accused on April 4, but a case was lodged against the victim instead. The accused, however, lodged a counter FIR against the woman’s family.The officer said that after talking to both sides it was found that there was a dispute between the two parties over the past 10 to 12 years, with cases registered against each other.Alleges conspiracyMr. Sengar dismissed the allegations of gang rape, saying it was “a conspiracy” against him.According to the Unnao police, on the complaint of the woman, a case was registered at the Mankhi police station under Sections 363 (kidnapping) and 366 (kidnapping a woman to compel her for marriage) of the Indian Penal Code on June 20, 2017.After an investigation, the police added Section 376 D (gang rape) of the IPC and Section 3/4 of the POSCO Act to the case.Three accused, Shubham Singh, Brijesh Yadav Awadh Narayan, were arrested following this. The charge sheet was also submitted in court.last_img read more

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Dehydration

first_imgDefinitionDehydration occurs when your body does not have as much water and fluids as it should.Dehydration can be mild, moderate, or severe, based on how much of your bodys fluid is lost or not replaced. Severe dehydration is a life-threatening emergency.CausesYou can become dehydrated if you lose too much fluid, dont drink enough water or fluids, or both.Your body may lose a lot of fluid from:Sweating too much, for example, from exercising in hot weatherFeverVomiting or diarrheaUrinating too much (uncontrolled diabetes or some medications, like diuretics, can cause you to urinate a lot)You might not drink enough fluids because:You dont feel like eating or drinking because you are sickYou are nauseatedYou have a sore throat or mouth soresOlder adults and people with certain diseases, such as diabetes, are also at higher risk for dehydration.SymptomsSigns of mild to moderate dehydration:ThirstDry or sticky mouthNot urinating muchDarker yellow urineDry, cool skinHeadacheMuscle crampsSigns of severe dehydration:Not urinating, or very dark yellow or amber-colored urineDry, shriveled skinIrritability or confusionDizziness or lightheadednessRapid heartbeatBreathing rapidlySunken eyesListlessnessShock (lack of blood flow through the body)Unconsciousness or deliriumExams and TestsYour health care provider will look for these signs of dehydration:Low blood pressureBlood pressure that drops when you stand up after lying downWhite finger tips that dont return to a pink color after your doctor presses the fingertipSkin that is not as elastic as normal. When your health care provider pinches it into a fold, it may slowly sag back into place. Normally, skin springs back right away.Rapid heart rateYour doctor may do lab tests:advertisementBlood tests to check kidney functionUrine tests to see what may be causing dehydrationOther tests to see what may be causing dehydration (blood sugar test for diabetes)TreatmentTo treat dehydration:Try sipping water or sucking on ice cubes.Try drinking water or sports drinks that contain electrolytes.Do not take salt tablets. They can cause a serious complication.Learn what to eat if you have diarrhea.For more severe dehydration or heat emergency, you may need to stay in a hospital and receive fluid through a vein (IV). Your health care provider will also treat the cause of the dehydration.Dehydration caused by a stomach virus should get better on its own after a few days.Outlook (Prognosis)If you notice signs of dehydration and treat it quickly, you should recover completely.Possible ComplicationsIf untreated, severe dehydration may cause:DeathPermanent brain damageSeizuresWhen to Contact a Medical ProfessionalYou should call 911 if:The person loses consciousness at any time.There is any other change in the persons alertness (for example, confusion or seizures).The person has a fever over 102 F.You notice symptoms of heatstroke (like rapid pulse or rapid breathing).The persons condition does not improve or gets worse despite treatment.PreventionDrink plenty of fluids every day, even when you are well. Drink more when the weather is hot or you are exercising.If anyone in your family is ill, pay attention to how much they are able to drink. Pay close attention to children and older adults.Anyone with a fever, vomiting, or diarrhea should drink plenty of fluids. DO NOT wait for signs of dehydration.If you think you or someone in your family may become dehydrated, call your health care provider. Do this before the person becomes dehydrated.ReferencesChen L. Infectious diarrheal diseases and dehydration. In: Marx JA, Hockberger RS, Walls RM, et al, eds. Rosens Emergency Medicine: Concepts and Clinical Practice. 7th ed. Philadelphia, Pa: Mosby Elsevier; 2009:chap 171.Greenbaum LA. Deficit therapy. In: Kliegman RM, Behrman RE, Jenson HB, Stanton BF, eds. Nelson Textbook of Pediatrics. 19th ed. Philadelphia, Pa: Saunders Elsevier; 2011:chap 54.Santillanes G. Claudius I. Rehydration Techniques in Infants and Children. In: Roberts: Roberts and Hedges Clinical Procedures in Emergency Medicine. 6th ed. Philadelphia, Pa: Saunders Elsevier; 2013:chap 19.Review Date:8/22/2013Reviewed By:Neil K. Kaneshiro, MD, MHA, Clinical Assistant Professor of Pediatrics, University of Washington School of Medicine. Also reviewed by David Zieve, MD, MHA, Bethanne Black, and the A.D.A.M. Editorial team.last_img read more