Saturday, February 23, 2019

Can AI Replace Your Manager?

        As AI capabilities grow and even some managerial positions are at risk for total automation. 
In 2018, Amazon abandoned development on a smart recruiting AI tool. Up until it was scrapped, this AI algorithm was considered the state of the art until it took a turn. As a learning machine, the AI was fed ten years worth of resumes to help identify patterns in successful hires; the only issue was this had been a predominantly male-dominated industry. The end result was a biased and sexist machine that began to favor male applicants over female applicants, even going so far as to filter out female names and applicants listing all-women colleges. However, the AI excelled in identifying patterns, good fits for applicants and positions, and organizing piles and piles of resumes to make suggestions. But hiring is another story. Machines don’t have the emotional capacity, the human touch, to get a personal feel for a potential hire, that is best left up to talented and intuitive hiring managers. 

What we learn from the failed Amazon AI project is not the failings of AI itself, but we have learned how well it can work alongside a human manager. By picking up the slack of paperwork, managers can focus on the candidates themselves.  It also reveals to us how AI manage data on a scale that no person ever could, but the lack of human touch poses its own issues - and opportunities.

Today, payroll managers and paymasters face a 96% chance of automation, going against many predictions that it would only be service industry positions and other predictable tasks that could see an overhaul of this kind. Gathering data, analyzing said data, and spitting out solutions is what this kind of AI does best and it seems to be creeping into management roles. 

Ask any smart manager and they’ll be the first to tell you how valuable humanity it when it comes to being a leader. Yet it seems more and more office managers and project managers find themselves inundated with repeatable, even mindless tasks. From ordering new office supplies to coordinating with the weekly cleaning crew, sometimes a manager is left with little time to actually manage. Wherever we go, tech seems to pick up our slack. Taking over the menial, the time-consuming, and the repeatable tasks of daily work operations save us time and valuable energy.

From a manager’s perspective, this absolutely changes the game. But making this kind of digital switch isn’t always easy, especially for small businesses with limited staff, limited resources, and limited funds. Around one in five small business leaders think the process of selecting and implementing new tech is just not worth the hassle and more than one in three think they don’t even need more tech. When it comes down to brass tacks, four in five small business believe they could benefit from better tech - as long as it’s the right kind of tech. Business leaders should ask themselves some key questions - What daily responsibilities consume too much time, and what gets overlooked with it’s crunch time? What could operations benefit from most, time saving tech, data processing automation, maybe even mobile access support?

Daily office operations and the responsibilities of office managers get an auxiliary boost from programs like Managed By Q. Ordering office supplies, scheduling maintenance, and cleaning, can all be done with the touch of a button. Furthermore, built-in hiring algorithms to find receptionist, assistants, and other staff can easily be reached through the iPad hub it works from. Today, more than half of small businesses are using some form of tech to help with the hiring process. Project management gets streamlined and simplified as well as AI platform iCEO takes on big projects and shrinks them down to size in easily achievable and scalable pieces. By the deadline of a project, iCEO is capable of generating a research report of up to 124-pages. Other AI programs from hiring to scheduling are available, but there really is no one-size-fits-all AI-bot for business. Ready to find the perfect fit for your business and get back to doing what matters? Let this infographic be a guide to AI management tools, its capabilities, weaknesses, and how to find the most sustainable and scalable option for any business.

Infographic: Can AI replace your manager?

Wednesday, February 13, 2019

Mexican Drug Lord: El Chapo Joaquín Guzman's Trial

Joaquin 'El Chapo' Guzman being escorted to a helicopter by Mexican security forces at Mexico's International Airport in Mexico City, Mexico, on 22 Feb 2014. Mexico's apprehension of the world's most-wanted drug boss struck a blow to a cartel that local and U.S. authorities say swelled into a multinational empire, fueling drug war and killings around the world. Photo: Susana Gonzalez. 
Mexican drug kingpin Joaquín "El Chapo" Guzmán has been found guilty on all 10 counts at his drug-trafficking trial at a federal court in New York.

Joaquín Archivaldo Guzmán Loera, the infamous drug lord known as “El Chapo,” was found guilty on all counts against him and now faces a lifetime in prison, ending a remarkable fall for a kingpin who spent years evading law enforcement officials while they say he continued amassing power and wealth atop a sprawling empire. Guzmán gained worldwide notoriety for the reach of the Sinaloa cartel, which prosecutors have called “the world’s largest and most prolific drug trafficking organization,” and for his own audacious escapes from Mexican prisons. He spent years on the run, assembling what U.S. authorities have described as a private army. Following his most recent prison escape in 2015, using a tunnel dug to his cell, he was hunted, rearrested again and then extradited to the United States, where he faced federal charges in multiple locations.


Who is he? 

Joaquín Guzmán Loera “El Chapo,” was born in La Tuna , Badiraguato, Mexico in 1957, his father was a farmer who grows opium, the only product planted in Badiraguato. El Chapo Guzmán he got first exposure to drugs and hardcore while a teenage of 7 years working in the marijuana and opium poppy fields. After that, when he was 15 he served an apprenticeship of sorts under Miguel Angel Félix Gallardo (The Godfather) and the chief of the most powerful Guadalajara cartels, that spun the drug wars in Latin America. 

His rise was swift, setting up his own cartel, the Sinaloa, in north-west Mexico in the late 1980s. Over time, it became one of the biggest traffickers of drugs to the US. El Chapo narrowly escaped assassination from by a rival gang's shootout in 1993. The Mexican attorney general's office described him as "egocentric, narcissistic, shrewd, persistent, tenacious, meticulous, discriminating and secretive", According to New York Magazine

   El Chapo' Guzman escorted to a helicopter by Mexican security unit. Photo: Susana Gonzalez. 
He was arrested by Mexican authorities and sentenced to 20 years in jail, but escaped and eventually apprehended again. His verdict was unanimous read out by a jury in Brooklyn in a packed courtroom on Tuesday, following an 11-week trial that ended. Guzmán, was wearing a dark suit jacket and tie and showed no visible sign of emotion as the verdict was announced, CBS News reported. 

Guzmán’s conviction came after prosecutors assembled an extensive case that included cooperating witnesses and intercepted messages, which demonstrated a remarkable degree of penetration into most secretive and dangerous cartel’s circles. John A. Horn, a former U.S. attorney who prosecuted Mexican cartel cases, he said a conviction in a case like this also carries a deeper meaning. “There does need to be a conviction of somebody like Chapo Guzmán, both for the symbolism and the pure factor of justice being served,” Horn, who is now in private practice, said in an interview before the verdict. “It does show that . . . for somebody at his level, justice will be done, it will be served. It’s an incredibly powerful victory for DOJ, for law enforcement.”

Prosecutors were unsparing in depicting Guzmán as a purveyor of brutality and horror spanning borders. “Guzmán Loera’s bloody reign atop the Sinaloa Cartel has come to an end, and the myth that he could not be brought to justice has been laid to rest,” said Richard P. Donoghue, the U.S. attorney for the Eastern District of New York, in a statement. “Today, Guzmán Loera has been held accountable for the tons of illegal narcotics he trafficked for more than two decades, the murders he ordered and committed, and the billions of dollars he reaped while causing incalculable pain and suffering to those devastated by his drugs.”

Acting attorney general Matthew G. Whitaker said in a statement: “This case, and more importantly, this conviction, serves as an irrefutable message to the kingpins that remain in Mexico, and those that aspire to be the next Chapo Guzmán, that eventually you will be apprehended and prosecuted.” But defense attorneys insisted that he has been made a scapegoat. Guzmán’s lawyers asked the jury to dismiss the testimony of the government’s cooperating witnesses, describing them as liars out to save themselves by seeking the best possible deals with authorities. 

Guzmán’s lawyer, A. Eduardo Balarezo, said in a statement that he is disappointed by the jury’s verdict and will consider all options, including a possible appeal. “We were faced with extraordinary and unprecedented obstacles in defending Joaquín,” Balarezo said, including Guzmán’s detention in solitary confinement, massive amounts of discovery that were difficult to review in a timely manner and the government’s reliance on cooperating witnesses, which he said “laid bare the corruption of the criminal justice system where freedom is traded by the government in exchange for testimony.”

For Guzmán, a conviction in a U.S. courtroom that guarantees life in prison cuts to the heart of his underworld myth, which only grew while he was a notorious fugitive and mysterious prison escapement. Federal prosecutors have described Guzmán’s rise in the 1980s as being fueled by his skill at funneling cocaine into the United States and then getting proceeds back to Colombian cartels. Guzman continued expanding his empire, prosecutors said, even after he was taken into custody in Guatemala in 1993 and in 2015 was placed in a maximum-security Altiplano prison, he managed to get away every time with insider assistance. 

His escape from prison in — infamously said to involve him slipping away in a laundry hamper — began what would be more than a decade evading capture. Those years were filled with financial successes, violence and efforts to corrupt Mexican government officials, prosecutors said in court filings. They also said Guzmán and his associates obtained drugs and supplies from other countries and sent cocaine, heroin, methamphetamines and marijuana into the United States. When his days getting closer in 2016, he was arrested once more and spent a year in custody before his extradition in 2017. 

The drug trade was a gold mine for Guzmán, enabling him to “exponentially increase his profits to staggering levels,” prosecutors said in one court filing. But a key part of that, prosecutors continued, was “thousands of acts of violence” — including murder, torture and kidnappings — committed by assassins and aimed at possible witnesses or people who sought to help law enforcement.


Prosecutors say Guzmán carried out some of the violence personally. During closing arguments in the trial, Assistant U.S. Attorney Andrea Goldbarg said Guzmán once cursed and shot two men, both of whom were already badly beaten, for working with a rival cartel. He then ordered their bodies thrown into a bonfire, Goldbarg said. An attorney for Guzmán said he denied the allegations. 

While Guzmán had sought to shield his communications from authorities, he also wiretapped people around him — including his family, mistresses and other associates — which Goldbarg said ultimately helped law enforcement officials. The IT technician who set up a system for Guzmán to surveil those around him gave it to the FBI. Goldbarg said Guzmán found out the technician was working with U.S. authorities and sought to have him killed, but no one could find him. The technician testified at trial.


Emma Coronel Aispuro, a wife of Joaquin 'El Chapo' Guzman surrounded by security personnel and members of the media taking coverage as she exits the U.S. District Court of Eastern New York City on 12 February 2019 in the Brooklyn.  (Photo by Drew Angerer/Getty Images). 
El Chapo wife Emma Coronel
Emma Coronel Aispuro wife of 'El Chapo' speaks on 04 February 2019 in the Brooklyn borough of New York City. During the deliberations in the trial of El Chapo, who is accused of trafficking over 440,000 pounds of cocaine, in addition to other drugs, and exerting power through murders and kidnappings as he led the Sinaloa Cartel.
 As he was escorted from the courtroom, Guzmán shook the hands of his lawyers before exchanging glances with his wife, Emma Coronel, a 29-year-old former beauty queen, and giving her the thumbs up. Judge Brian Cogan, who presided over the trial, thanked the jurors for their dedication at what he described as a complex trial, saying it was "remarkable and it made me very proud to be an American". Guzmán's lawyers said they planned to launch an appeal.

Another court papers accused him of having girls as young as 13 drugged before raping them. Guzmán "called the youngest of the girls his 'vitamins' because he believed that sexual activity with young girls gave him 'life'", a former associate, Colombian drug trafficker Alex Cifuentes, was quoted as saying. During the trial, Cifuentes also alleged that Guzmán gave a $100m (£77m) bribe to former Mexican President Enrique Peña Nieto, who is said to have contacted him after taking office in 2012 and asked for $250m in return for ending a manhunt for him. Mr. Peña Nieto has not publicly commented. When asked by a former cartel lieutenant why he killed people, he is alleged to have said: "Either your mom's going to cry or their mom's going to cry."

How he escaped from prison? 

His sons bought a property near the prison and a GPS watch smuggled into the prison gave diggers his exact location. At one point Guzmán complained that he could hear the digging from his cell. He escaped by riding a specially adapted small motorcycle through the tunnel. He also used the software on his phone to spy on his wife and mistresses, which allowed the FBI to present his text messages in court. In one set of texts, he recounted to his wife how he had fled a villa during a raid by US and Mexican officials, before asking her to bring him new clothes, shoes and black moustache dye.

Among the drug cartel circles, he had the status of a folk hero, a popular subject of "narcocorridos" - there are musical tributes, Hollywood movies, and cigarette package portraying this drug baron. In 2009 Guzmán entered Forbes' list of the world's richest men at number 701, with an estimated worth of $1bn (£775m).




Monday, February 11, 2019

Russia To Disconnect Itself From The Internet

Russian President Vladimir Putin prepares to answer questions during a special interactive webcast, organised by BBC and Yandex in Moscow, 06 July 2006. Photo: Dmitry Astakhov/TASS. 
Russian authorities and major internet providers are planning to disconnect the country from the internet as part of a planned experiment, Russian news agency RosBiznesKonsalting (RBK) reported last week. The reason for the experiment is to gather insight and provide feedback and modifications to a proposed law introduced in Duma the Russian Parliament in December 2018. A first draft of the law mandated that Russian internet providers should ensure the independence of the Russian internet space (Runet) in the case of foreign aggression to disconnect the country from the rest of the internet.


In addition, Russian telecom firms would also have to install "technical means" to re-route all Russian internet traffic to exchange points approved or managed by Roskomnazor, Russia's telecom watchdog. Roskomnazor will inspect the traffic to block prohibited content and make sure traffic between Russian users stays inside the country, and is not re-routed uselessly through servers abroad, where it could be intercepted. 

A date for the test has not been revealed, but it's supposed to take place before 01 April, the deadline for submitting amendments to the law --known as the Digital Economy National Program, that budget for $15 billion to 2024. The test disconnect experiment has been agreed on in a session of the Information Security Working Group at the end of January. Natalya Kaspersky, Director of Russian cyber-security firm InfoWatch, and co-founder of Kaspersky Lab, presides over the group, which also includes major Russian telcos such as MegaFon, Beeline, MTS, RosTelecom, and others.

 Kaspersky Lab Global Partner Conference for Cybersecurity To Cyber Defense on 30 May 2018 in Saint Petersburg, Russia. Photograph: Credited to Ian Gavan/Kaspersky Cyber-security Lab.
RBK reported that all internet providers agreed with the law's goals, but disagreed with its technical implementation, which they believe will cause major disruptions to Russian internet traffic. The test disconnection would provide ISPs with data about how their networks would react. Finanz.ru also reported that local internet services Mail.ru and Yandex.ru were also supportive of the test disconnection. The Russian government has been working on this project for years. In 2017, Russian officials said they plan to route 95 percent of all internet traffic locally by 2020. Authorities have even built a local backup of the Domain Name System (DNS), which they first tested in 2014, and again in 2018, and which will now be a major component of the Runet when ISPs plan to disconnect the country from the rest of the world.


Russia's response comes as NATO countries announced several times that they were mulling a stronger response to cyber attacks, of which Russia is constantly accused of carrying out. The proposed law, fully endorsed by President Putin, is expected to pass into a national law. Ongoing discussions are in regards to finding the proper technical methods to disconnect Russia from the internet with minimal downtime to consumers and government agencies. The Russian government has agreed to foot the bill and to cover the costs of ISPs modifying their infrastructure and installing new servers for redirecting traffic towards Roskomnazor's approved exchange point. The end goal is for Russian authorities to implement a web traffic filtering system like China's Great Firewall, but also have a fully working country-wide intranet in case the country needs to disconnect.

Wednesday, January 30, 2019

AI and Genomic Medicines

 An intermarriage of Biomedical Imaging Technology with Artificial Intelligence.Photo/iStock. 
AI genomic medicine aims to develop qualitative drugs by using deep learning techniques to find related or contrast patterns in genomic and medical data

The next blockbuster drug could be developed with help from machine-learning techniques that are rapidly developing from AI research which aiming to enhance pharmacology labs. The utility of artificial intelligence (AI) has been explored in a multitude of industries (transportation, communication, security) with the healthcare industry being a core focus of AI research in 21st century. Healthcare is a complex industry and AI needed for each component can be varied. The Artificial intelligence involved in healthcare would segregate pharmaceuticals needs from the rigidity perspective of general practitioner, patient, regulatory authority, or health management system. A pharmaceutical based AI projects are on the forefront, most focus in two areas: Patient care platform specifically for diagnostics and therapeutic (drug discovery).


What exactly is artificial intelligence, and how can we use it?


The phrase “artificial intelligence” is unquestionably and undoubtedly, the technology is doing more than ever we knew or realize — whether for both good and bad. It’s already being deployed in health care and warfare; it’s currently assisting people effortlessly make music, write books and conference management; it’s scrutinizing your personal resume, judging your creditworthiness, and tweaking the photos you take on your phone or share on social media. In short, it’s making decisions that affect your life whether you like it or not.  In simplistic terms, AI itself is an algorithm and this can be in the form of software/hardware or application that has the capability to utilize massive amounts of data for multiple tasks. Machines have an advantage over human's brain memory capacity for store, access, and process limitless volumes of information in their memory and apply it quickly to a predefined task (or application).

AI and Drug Discovery: The process of drugs production can take years or even more to come to the market. It cost billions, and can even ruin a company if they fail in late-stage trials having poured in so much investment. The introduction of Artificial Intelligence and the autonomic concept has becomingly more and more important in addressing these issues and this it shows that AI increasingly is the future of drug discovery. The commercial drugs demand faster and better drug discovery as well as delivery.

Artificial Intelligence (AI) in the Pharmaceutical industry and its future innovations.  
Perhaps the most obvious application of artificial intelligence in pharma is using its ability to quickly ‘read’ vast amounts of scientific data: research published in journals, as well as patient records and tissue/blood samples, and using patterns in the data to make scientific hypotheses which can direct pharma companies’ drug development. The speed of AI in these processes allows companies to develop drugs based on biological markers, with greater accuracy, rather than the scatter gun approach of chemical screening. In this way, companies can be zeroing on particular indications which the drug is most likely to successfully treat.
The ever rising costs in drugs research and development, with the frustratingly long time spent in bringing new novel drugs to market and the high rate of failure in the processes needs to be tackled.
Boston-based biotech Berg’s Niven Narain says the company’s AI platform, Interrogative Biology, allows researchers to ping a look into 14 trillion data points in just one single tissue sample. Narain stated that artificial intelligence will halve the time (and potentially the cost). Berg soon will enter its candidate BPM31510 to the market. Similarly, IBM’s Watson supercomputer is currently conducting AI-based trials where it scans mutation data from the tumors of 20 brain cancer patients. This is something that usually would take human scientists several weeks or months to analyze, but Watson can do the same in a matter of minutes.


Through machine learning, Watson gets the process done in better and faster. Ultimately, the screening process could be fast enough to analyze the entire genome of each patient’s individual cancer and for treatments to be tailored based on its specific mutations if they exist. If not, there will be a company interested in putting that right. In the UK, the University of Manchester’s AI platform, known as Eve, can screen more than 10,000 compounds in a day, matching them to likely targets. Again, through machine learning and hypothesis testing, Eve recognizes why ‘she’ has succeeded, and so gets faster the more screening she performs. Lower drug pricing Cheaper drug development should enable cheaper prices. Drug pricing is a hugely controversial issue in the industry nowadays, and the reputations of pharma companies are suffering as a result of massive price hikes. Pharma investors will often justify such increases by citing the huge costs of researching and development, so if such costs can be significantly reduced – as Narain has suggested the cost-effective artificial taking over the pharma – possibly they will no longer be able to use this justification, and prices should (in theory) fall.

 AI can help pharma companies, but right up to approval and even in the general running of the companies. After a promising candidate is discovered, AI could be used to design more effective clinical trials and more quickly analyse the data that emerges from them. Even business decisions may be handed over to supercomputers. Consider the huge numbers of mergers and acquisitions already tendered. AI could more effectively analyse potential synergies gained from the merger of particular companies, allowing them to decide if a combination is worthwhile. If it is, then AIs can help make decisions on integrating the R&D departments, for example. It could also have a hand in the digital sales and marketing process.

In 2015 Eularis released a cloud-based marketing analytics platform for the pharma industry, backed by cutting edge algorithms and the same machine learning capability used in Waymo, which is Google’s driverless cars. These type of AI applications have the abilities to learn the input/output stimuli and effectively mimic them and apply them to new products. The application of AI in pharma is in its infancy, and it could take two decades to reach its full potential. However, the beginnings of a technical revolution that could change the way in which drugs are brought to market appear to have begun already behind the closet and backstage of medical labs, which is good news for pharma companies and patients alike. Where there is data to be analyzed or a business decision to be made, the betting is that the AIs of the future will challenge any current pharma executive to do it better and faster.



The Massachusetts Institute of Technology (MIT) has compiled the “Machine Learning for Pharmaceutical Discovery and Synthesis Consortium.” The group forged collaboration between the pharmaceutical and biotechnology industries and the departments of chemical engineering, chemistry, and computer science at MIT. The goal of the collaborative efforts is to facilitate the design of useful software for the automation of small molecule discovery and synthesis. Pharma companies currently involved in the consortium include are:
  • Amgen
  • BASF 
  • Bayer 
  • Lilly
  • Novartis
  • Pfizer
  • Sunovion
IBM AI research's Watson for Drug Discovery delivers is a cognitive platform and natural language processing trained in the life sciences domain. This AI-based approach facilitates the drugs analysis and processing a massive amount of database more comprehensively and faster than simple search tools or unaided research teams.

Dr. Robert Bowser (Ph.D) the steering hand on IBM Watson Drug Discovery research.  
As according to the Deep Genomics, a Canadian company that uses machine learning to trace potential genetic causes for disease, announced that it’s getting into drug development. It joins a growing list of AI companies betting that their techniques can help produce powerful new drugs by finding subtle signals in huge quantities of genomic data. Deep Genomics was founded by Brendan Frey, a professor at the University of Toronto who specializes in both machine learning and genomic medicine. His company uses deep learning, or very large neural networks, to analyze genomic data. Identifying one or more genes responsible for a disease can help researchers develop a drug that addresses the behavior of the faulty genes. The company will focus, at first, on early-stage development of drugs for Mendelian disorders, inherited diseases that result from a single genetic mutation. These diseases are estimated to affect 350 million people worldwide.

The paradigmatic shift of AI application to the medical world and drug development is partly encouraged by the emergence of some powerful new algorithms, in the market which is cost-effective and new fresh ways of sequencing whole genomes and be able to read out entire DNA genome at once. “There’s an opening of a new era of data-rich, information-based medicine,” Frey says. “There’s a lot of different kinds of data you can obtain today in a brief short period. And the best technology we have for dealing with large amounts of data is none then the machine learning and artificial intelligence.” Deep learning has emerged in recent years as a very powerful way to find abstract patterns using large amounts of training data. It has proved especially valuable for speech recognition and for classification (see “10 Breakthrough Technologies 2013: Deep Learning”). The approach is now rapidly finding new uses in major fields, where it offers a way to spot signs of disease in medical images for predicting disease from a medical record of the patients.

Frey, who trained as a computer scientist and studied at the University of Toronto under Geoffrey Hinton, a key figure in the development of deep learning, says Deep Genomics will seek to partner with a pharma company on drug development. But he adds that the company offers key expertise. “There’s going to be this really massive shake-up of pharmaceuticals,” Frey says. “In five years or so, the pharmaceutical companies that are going to be successful are going to have a culture of using these AI tools.”  The company has published work showing how deep learning can help identify patterns in DNA that might contribute to diseases such as spinal muscular atrophy and nonpolyposis colorectal cancer.

Stephan Sanders, an assistant professor at UCSF School of Medicine in San Francisco who also specializes in using genomics and bioinformatics to study disease, says deep learning could help with drug development by finding patterns in sparse pathology data combined with large genomic data sets. “We have vast amounts of data; three billion data points per individual,” Sanders says. “What we have less of is the other end: clean data of phenotypes or outcomes.”

Several other companies are seeking to apply machine learning to drug development. These include BenevolentAI, a British AI company, and Calico, a subsidiary of Alphabet. Dr. Ken Mulvany, the founder of benevolent, says his company is focused on diseases of inflammation and neurodegeneration and rare cancers. The AI project it aims to tap into largely unused research data. “Developing medicines is still a very lengthy, risky, and expensive process with high rates of attrition,” Mulvany says. “[But] there is an enormous amount of untapped data located in pharma R&D organizations without any plans to develop it.”

Argumentative Assumptions: The world’s leading AI experts and developers stressed that AI should be used for the tedious and monotonous tasks along with human supervisory. Humans are generally well suited with the natural wisdom plus emotional intelligence to “do no harm”, but again our weakness as human beings is that we lack projectable memories and abilities to apply quick response in accessing the volumes of data that may be stored in our brains (subconscious repository) in correlating with a multitude of options or events given. AI has the potential to be ''Yin and Yang'' to a human for the check-balance.

The core differentiators between the capabilities of a human and that of artificial intelligence could be incorporated into a new hybrid model within the pharmaceutical industry whereby AI assists humans to carry out daily tasks with efficiency and expertise. AI may have the “intelligence” for excellence with critical, yet repetitive, tedious tasks such as identification of a new therapy, while human applies “wisdom” required to balance efficacy vs. adverse events.

References
  1. https://blog.leanix.net/en/artificial-intelligence-expert-systems
  2. https://medium.com/@miccowang/computer-vision-the-closet-thing-to-ai-on-our-personal-device-d2ff63994856 
  3. https://www.expertsystem.com/artificial-intelligence-system-examples/
  4. https://www.networkworld.com/article/3239146/internet-of-things/conventional-computer-vision-coupled-with-deep-learning-makes-ai-better.html 
  5. http://www.eetn.gr/index.php/about-eetn/eetn-publications/ai-research-in-greece/planning-and-scheduling http://mlpds.mit.edu/ 
  6. https://youtu.be/8cmx7V4oIR8 https://youtu.be/J6tgYBMXR6s
  7. https://www.drugtargetreview.com/article/15400/artificial-intelligence-drug-discovery/
  8. http://www.pharmafile.com/news/502337/what-could-artificial-intelligence-mean-pharma
  9. http://www.pharmtech.com/artificial-intelligence-key-process-understanding How Data Analytics And Artificial Intelligence Are Changing The Pharmaceutical Industry
  10. https://www.disruptordaily.com/top-10-artificial-intelligence-companies-disrupting-pharmaceutical-industry/ 
  11. http://social.eyeforpharma.com/clinical/artificial-intelligence-brave-new-world-pharma
  12. http://www.pharmtech.com/artificial-intelligence-key-process-understanding How Data Analytics And Artificial Intelligence Are Changing The Pharmaceutical Industry.
  13. https://www.forbes.com/sites/forbestechcouncil/2018/05/10/how-data-analytics-and-artificial-intelligence-are-changing-the-pharmaceutical-industry/#3727cbd63644 Artificial Intelligence Already Revolutionizing Pharma
  14. http://www.pharmexec.com/artificial-intelligence-already-revolutionizing-pharma
  15. https://www.pharmaceuticalonline.com/doc/what-to-expect-from-artificial-intelligence-in-pharma-and-how-to-get-there-0001 
  16. https://www.analyticsinsight.net/how-artificial-intelligence-is-disrupting-speech-recognition/
  17. https://youtu.be/6tBZA2rygcM 
  18. https://www.linkedin.com/pulse/machines-learning-robots-arent-coming-your-compliance-nathan-lynch/ 
  19. https://www.forbes.com/sites/forbestechcouncil/2018/07/02/what-is-natural-language-processing-and-what-is-it-used-for/#238ea9805d71
  20. http://www.pharmexec.com/ai-pharmaceuticals




Thursday, January 24, 2019

Can A Human Teach AI Ethics?

Google AlphaZero AI is taking humans one step closer to the Technological Singularity.  
What happens if artificial intelligence surpasses human intelligence? Experts are ever discuss and debating about the potential risks and as well as the great opportunities presented by modern technology especially AI which is still advancing and that it may outpaces the cognitive capacity of human being. Smart robots have became part of our daily fa-fairs. AI already took over the chessboard —up-next is to take the boardroom for commanding humans. Google’s AlphaGo is no ordinary cyborg but the first artificial intelligence that teach itself how to dominate the chess game without any human intervention. Did you heard this? It is evidently to state here that the technological singularity is ever unfolding closer.


AlphaZero is more advanced, developed with a “superhuman technical capability” in mastering the chess game in just four hours. Essentially, the AI system learns the tricks by absorbing humans' knowledge include entire history of chess game in sixth fraction of a day — and then figured out how to tackle anyone or anything. AlphaZero programmed with the rules of the game (not the strategy) AlphaZero played 100 games against Stockfish, the world champion chess program ever known. AlphaZero won 25. The last 72 games resulted into a draw with AlphaZero recording no losses and Stockfish recording no wins. “Now we know who is our new lord ,” David Kramaley, the CEO of chess science stated on the website Chessable. “It will no doubt that AI revolutionized the chess game, but think about this in a great picture; this means the its algorithm could be applied outside chess. The algorithm could run cities, continents, universes.” Google’s DeepMind lab has been working on this same technology for years, beginning with AlphaGo,which learned how to play the Chinese board game.


AlphaGo AI stomped to victory for a fourth time against Go world champion Lee Sedol.  
Using "reinforcement learning technique", where AlphaZero plays itself over and over again starting from random play and without any human supervision or data, as according to Google's DeepMind researchers stated in their study. This allowed the system to improve and refine its digital brain, known as a neural network, as it continually learned from experience. This basically means that AlphaZero it's own teacher. The new version of AI is more powerful than AlphaGo because it is no longer constrained by the limits of human knowledge. This is a major achievement for AI, and the subfield of reinforcement learning in generally. When AI teaching itself, the system sprung beyond unimaginable expectations and exceeding human knowledge by an order of magnitude in a short time span, while also developing unconventional strategies and creative moves.
The time when humans can have a meaningful conversation with an AI has always seemed far off and the stuff of science fiction. But for Go players, that day is here.
The Challenge: No doubt that, AlphaZero in next 5 years it will protrude hefty impacts on the rest of the world! According to Nick Hynes, a grad student at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL), confirmed that these specialised tool will have an impact on our daily lives. So, the decisions regarding the responsible design of artificial intelligence (AI) are often made by engineers who receive little training in the complex ethical considerations at play within their designs. Universities have struggled to find effective ways to integrate these issues into curricula for technical students. At the World Economic Forum Global Future Councils on Artificial Intelligence and Robotics, that comprised of leading academics and experts in the field, has recommended the creation of actionable AI map, social inquiry and as well as AI discourse.


The Opportunity: The project under AI world map seeks to link all professors from around the world and offers them a platform to share, learn and modify their curriculum to include a focus on AI ethics. This open-source sharing database will enable instructors to teach future AI scientists to understand the potential consequences of their work, empower them to make the right decisions as they design and deploy new products, and strengthen the use of responsible AI for the benefit of those companies and governments in which they end up working with.

Social Impact: The creation of AI without consideration of ethics and values, social inclusion and human centered approach can have deleterious consequences to individuals, businesses, and entire social structures. As recently demonstrated by the very controversial, Cambridge Analytica,reflecting on the use of AI to sway election results: “He thrilled to the intellectual possibilities of it. He didn’t think of the consequences.” AI scientists must be taught at the earliest stages how to consider and address issues such as bias, privacy, transparency and accountability in the creation of AI App or bots.






Wednesday, January 23, 2019

China Becoming AI Superpower

Artificial Intelligence and Blockchain technology became a pivotal discussion at (WEF).  
Artificial intelligence (AI) has come to occupy an important role in Beijing daily settings, and one of China's 2030 important blueprint. China wants to be a global leader in this particular field by 2030. China has an edge in terms of academic researchers, patents, cross-border boot camps, and AI investment. 

In 2017, China published its “Next Generation Artificial Intelligence Development Plan”, which laid out plans to ultimately become the world leader in artificial intelligence, with a domestic AI industry worth almost US$150 billion. The first step of that plan is to catch up with the US on AI technology and applications by 2020. China now dominates AI funding. Last year, 48 percent of total equity funding of AI start-ups globally came from China, compared to 38 percent funded by the US, and 13 percent by the rest of the world. This is a significant jump from the 11.3 percent of global funding China made in 2016. 

China’s AI race

The Chinese AI industry has grown 67 percent over the past year and produced more patents and research papers than the US. This is despite having access to about a fifth of America’s talent pool. China also has an edge over the US on applying AI technology to specific areas such as unmanned retail and medical diagnosis.


Top business leaders are attending at the World Economic Forum in Davos, Switzerland are monitoring China’s slowly growing economy, but they are eager to make another point: China has taken the lead on the artificial intelligence revolution. Blackstone chief executive Stephen Schwarzman, who travels frequently to Asia, said he sees an explosion of new AI businesses in China. “When I go to China, there’s almost an endless stream of people who are showing up developing new companies. The venture business there in AI-oriented companies is really exploding with growth,” Schwarzman said on a panel.

The Chinese government has made tech dominance a priority in its “Made in China 2025” plan. Chinese leaders are pouring government money into AI research and development in a scientific push that has been compared to the space race or the Manhattan Project that the U.S. government-funded during World War II to develop a nuclear weapon. There are concerns that the United States is falling behind, and executives might not even realize it. [Japanese Prime Minister takes swipe at Trump in Davos] For the first time this year, consulting firm PwC used its annual CEO survey to ask global business leaders whether they thought AI would have a larger impact than the Internet. Eighty-four percent of Chinese executives said AI would be bigger than the Internet, while 38 percent of American executives said the same. The startling difference in views about AI surprised consultants at PwC who said attitudes are rarely this divergent among the nearly 1,400 global executives they survey each year.

“It’s rare you get that big of a difference between two superpowers,” said Tim Ryan, U.S. chair of PwC. “It tells us we [in the United States] probably need to make sure we’re thinking about it the right way because you saw a very big difference between our two countries on that question.”

Baidu, Alibaba and Tencent budded as 'BAT' are three Chinese tech giants and AI leaders.  
The survey asked executives how widely they had deployed AI initiatives in their company. China was by far the leader with a quarter of Chinese business leaders saying AI was utilized on a wide scale at their firm. Only 5 percent of U.S. executives said the same. Most American companies are running AI pilot projects, Ryan said, but they haven’t scaled the initiatives up in the way the Chinese have.


The United States isn’t investing as much money in China into AI, which might explain some of the difference, PwC said, but there are also ethical and skills challenges to work through before widespread adoption. “I would say the U.S. has the potential to fall behind if the CEOs and the education and the government don’t start doing more to actually do the upskilling,” said Bob Moritz, PwC chairman.

Executives from the world’s leading tech companies urged business leaders to focus more on AI. The U.S. government has mostly focused on AI from a military context. The Pentagon announced in June that it was establishing a new Joint Artificial Intelligence Center that will spend $1.75 billion over six years, but experts say it’s a small fraction of the scope of China’s investments. “The ability to apply AI to every industry, every company is going to new lines of revenue.

 It’s going to increase operating efficiency,” said Ruth Porat, chief financial officer of Alphabet, the parent company of Google. “To the extent, you are not embracing it now, you are slowing down the ability to actually continue to scale in any market globally." Liang Hua, chair of Chinese telecom giant Huawei, told a small group of reporters that “computer power will be the new electricity” and that his company had recently released a “cost-effective” AI chip and was working on other AI and machine-learning initiatives.

“This is the start of a revolution that’s going to change enormous amounts of things from the workplace, jobs, what people do, development of knowledge in the absolute. It’s very exciting,” Schwarzman said.  In the early 2000s, China began to build a high-speed rail network that the government said would spur technological development and improve the country’s transportation system. 

This train network is now one of the most advanced on the planet. There are good reasons to believe the country can make its vision of AI supremacy a reality. China’s AI Plan got three-steps: Firstly, it must be able to keep pace with all leading AI technologies, and its application in general context, by 2020. In Part two is to make major breakthroughs by 2025, which is intended to lead to the third part of the plan – the establish Chinese soft power as the world leader in the AI field by 2030.

(1) 2020 - Competitive in AI: Focus on big data intelligence, Autonomous intelligence systems, Cross-medium intelligence, Swarm intelligence, Hybrid enhanced intelligence and AI Foundational theories.  (2) 2025 - Breakthrough: AI in medicine, City infrastructure, manufacturing, Agriculture, AI laws and regulations, Security assessment and control and Technical capabilities. (3) 2030 - Become World Leader: Focus on social governance, National defence AI integration and Industrial value chain.


Moritz said China, in particular, has a lot at stake if the AI revolution causes mass unemployment at factories. Chinese e-commerce giant JD.com already has a vast warehouse outside Shanghai that is almost entirely operated by robots. China has built factory towns that could become ghost towns. But the Huawei executive said his country can deal with those ramifications by creating new jobs. “While definitely, AI will play an important role in future society, we shouldn’t overstate that role,” Hua said. “Human beings will do what they are good at, and machines will also do what they are good at. It takes close collaboration between human beings and machines to achieve progress.”


Saturday, January 19, 2019

AI Is Leading Humanity Into Unknown Territory



In year 2025 human's works will be substituted by autonomous machines.  
In the year ahead, artificial intelligence (AI) is set to move into the mainstream as many new high-tech innovations are ready to make their debut. Creating a well-planned infrastructure for these high-tech products is essential for ensuring they have favorable outcomes and become major drivers of economic growth. Expected to yield benefits in terms of labor productivity and time-saving, AI could drive up the world’s gross domestic product if it is strategically implemented in accordance with consumer demand.


Redefining the way we live in ways never thought possible, many AI applications, such as self-driving cars and facial or speech recognition, have already been introduced. Having reached the level of sophistication needed for widespread implementation, in the months ahead many more such technologies will be employed for public use. As it is a virtual step into the unknown, it is time to evaluate the prospective impacts of some of these applications. First, AI can change the nature of social media, which has recently become effective at shaping opinions all over the world. Used to share information, news and views, social media platforms are already highly vulnerable to fake narratives and propaganda. 
As China embraces the next-level surveillance for its social credit system, we have to wonder at what facial recognition will bring us in the 2020s.
With the arrival of AI, social media can be further manipulated as it is possible to create fake visual material. Sophisticated deep-fakes can be used for lip-synching or face swaps. Yasmin Green, director of Jigsaw, an Alphabet company dealing with security, said, “Perhaps the most chilling realization about the rise of ‘deep-fakes’ is that they don’t need to be perfect to be effective. The world will confront a new falsified video deployed to deceive entire populations.” At the same time, audio can also be created with the help of AI to make it indistinguishable from genuine speech.

These uses of AI are bound to have far-reaching repercussions. For example, their use could raise questions about the credibility of recorded evidence in legal cases. Starting this year, this type of generative AI technology will be available for incorporation in new products. Second, we are already familiar with facial recognition, which is used widely in smartphones, smart cameras and online media. Though it is highly useful for personalizing gadgets, it has reduced privacy, and stringent regulations are expected for this ubiquitous application of AI, which is being rolled out for use in healthcare, autonomous vehicles, law enforcement, and many new domains. It all depends on which country focuses on which aspect of AI. For example, facial recognition applications are most common in China, where many tech companies use them for providing services.

Third, speech recognition is the next big thing, and Amazon’s Alexa has already become popular for searching information on the Web or for use in “smart homes.” The latest television sets and home appliances can be controlled by voice now and it is projected that as many as 66.6 million Americans will be using voice-recognition technology in 2019. At the same time, it will be possible to give verbal instructions to computers, while they can also be taught to see with image recognition.


Fourth, with the smartphone, medical information has become easier to collect as well as access, and there are bound to be some privacy issues. According to a study by the University of California, Berkeley, individuals can be identified by following their daily patterns on activity trackers in smartphones and smartwatches. Computers could be used for diagnosing diseases, analyzing data and verification, and this is only the beginning. As Professor Patti Maes of the MIT Media Lab said, “We are already dependent on our mobile technologies for many of our everyday tasks and goals, but we will also soon rely on digital technologies for the optimal functioning of our bodies.”

Finally, 2019 will also see the results of 5G (5th-generation wireless) technology, from autonomous cars to advanced robotics. The full-blown effects of AI will be evident for the first time, and it will be interesting to see how people take to smart homes, connected cars and other innovations in their day-to-day lives. Literally, the sky is the limit, as tiny satellites could be released into orbit to run telecommunications, such as the CubeSat,that can be launched into space at a very low cost. Another possibility for AI is its use in preventing and fighting off cyber threats and hackers, as it can pick up subtle indicators of any abnormal activity quite early.

However, there are some negative aspects already apparent, such as the legal complications that will arise in the event of a driverless car getting into an accident. If fatalities occur, the algorithm operator could face charges under “product liability” rules and not the owner of the car. AI is a new frontier, and there are many legal and ethical implications related to its widespread use. 

Considering the pros and cons, there is a need for these technologies to be gradually released after researching and deliberating over the regulatory side before the proto-AI technologies of today can evolve into true AI super-intelligence. Advancing rapidly since 2010, global AI investments have already increased by 60% and are expected to contribute a humongous $15.7 trillion by 2030.

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