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martes, 17 de abril de 2018

La IA sigue adelante en su proceso de inclusión en la sociedad.


Fuente: https://techcrunch.com/2018/04/16/sword-health/

Cada día que pasa, vemos más y más aplicaciones funcionales de la IA en la vida cotidiana. Algunos siguen pensando que es ciencia ficción o un fake news lo que leen en los medios digitales y escritos. Lo cierto es que estamos siendo "invadidos" por una serie de "nuevos accesorios inteligentes" que nos permitirán llevar una mejor calidad de vida y ampliar nuestra expectativas de vida en un presente cercano.

Les dejo este buen artículo sobre el tema.
Sword Health, una empresa emergente que opera en Portugal y ha desarrollado una solución digital de fisioterapia para permitir que los pacientes sean tratados de forma remota en sus propios hogares, ha recaudado $ 4,6 millones en fondos iniciales. Respaldando la ronda están Green Innovations, Vesalius Biocapital III y otros inversionistas no identificados en los Estados Unidos y Europa.


La compañía dice que usará el nuevo capital, que se suma a una subvención anterior de ~ $ 1.2 millones de la Comisión Europea, para acelerar el desarrollo de nuevas terapias digitales e impulsar el crecimiento global.

Utilizando lo que describe como una combinación de "sensores de movimiento de alta precisión" y los últimos avances en IA, la solución Sword Health tiene como objetivo hacer que la fisioterapia sea infinitamente más escalable, en reconocimiento de la escasez mundial de fisioterapeutas. Su producto estrella, "Sword Phoenix", ofrece a los pacientes ejercicios interactivos de rehabilitación física desde la comodidad de su propio hogar, supervisados ​​por fisioterapeutas remotos.

"Hace veinte años, mi hermano tuvo un accidente automovilístico. Lo que me di cuenta entonces (y esto sigue siendo cierto ahora) es que hay una gran brecha entre la demanda de fisioterapia y nuestra capacidad, como sociedad desarrollada, de ofrecer esa terapia ", dice el cofundador y CEO de Sword Health, Virgílio Bento.

"El problema es que la industria de la rehabilitación física no ha cambiado en los últimos 50 años. Seguimos dependiendo mucho de la interacción uno a uno entre paciente y terapeuta, que es el estándar de oro, pero no es un modelo escalable y en realidad es muy costoso tanto para los pacientes como para los proveedores de atención médica ".

Para remediar esto, Bento y el equipo de Sword comenzaron a trabajar en lo que él llama un concepto de "terapeuta físico digital". La idea es que al usar sensores de movimiento conectados a los lugares apropiados del cuerpo de un paciente, combinados con una interfaz de usuario impulsada por AI que pueda tomar esos datos de movimiento y dar retroalimentación instantánea, parte de lo que hace un fisioterapeuta puede ser aumentado por las máquinas.

"Con Sword Phoenix, los equipos clínicos extienden su huella terapéutica al hogar de cada paciente, amplían su alcance y pueden dedicar más tiempo a entregar el toque humano", dice.

Hasta la fecha, Bento dice que Sword está trabajando con compañías de seguros, servicios nacionales de salud, organizaciones de mantenimiento de la salud y proveedores en los EE. UU., Canadá, Australia, Noruega y el país de origen de la startup, Portugal.

"Estos clientes pueden proporcionar servicios de fisioterapia de mayor calidad directamente en el hogar del paciente y disminuir los costos operativos al mismo tiempo, un logro que solo es posible en el cuidado de la salud a través del uso inteligente del análisis de datos y la tecnología", agrega.

En términos de competencia, Bento argumenta que la mayoría de las compañías de tecnología para la salud se centran en el desarrollo de tecnologías que mejoren la interacción con el terapeuta de pacientes uno a uno (por ejemplo, Tyromotion, Hocoma). "Esta mejora incremental no es la solución porque no da como resultado un cambio de paradigma", dice.

Dicho esto, Bento reconoce que hay otras startups tratando de crear un terapeuta digital. Uno que he cubierto en detalle es Hinge Health respaldado por Atomico, que ha desarrollado una solución digital para trastornos musculoesqueléticos (MSK).

Oracle Hot Topics: ORA-1/10388 ON INSERT SELECT TO TABLE WITH QUERY HIGH


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My Oracle Support Site Alert: MOS Community Threads to be Searchable by Internet Search Engines


IMPORTANT NOTICE: 

As part of our continuous innovation process, starting June 1, 2018 Oracle will make select My Oracle Support Community information available to public search engines for indexing and search: My Oracle Support Community Space and Subspace name, thread title/header, thread post date and the first 100 words of the first thread post. Access by external search engines is limited to these attributes, and My Oracle Support login will still be required to access full Community content. Please note that this change applies retroactively for all existing post content as well as for all future posts. You must remove your existing posts using the instructions located here prior to June 1, 2018 if you do not want these posts searchable by third party search engines.

domingo, 8 de abril de 2018

Oracle Named a Leader in the 2018 Gartner Magic Quadrant for Identity Governance and Administration


Press Release 
Oracle Named a Leader in the 2018 Gartner Magic Quadrant for Identity Governance and Administration 
Oracle positioned as a Leader based on completeness of vision and ability to execute 

Redwood Shores, Calif.—Apr 2, 2018 

Oracle today announced that it has been named a Leader in Gartner’s 2018 “Magic Quadrant for Identity Governance and Administration” report for the fifth consecutive time. Oracle believes this recognition further validates the strength and innovation of cloud security services Oracle has introduced over the past year and its ability to help enterprises better integrate security solutions to manage their business. 

“Security and Identity has quickly become one of the most critical areas businesses must address in order to be successful and maintain regular operations, and identity governance is a critical foundational step any enterprise should take to strengthen its security posture,” said Eric Olden, senior vice president and general manager, security and identity, Oracle. “Over the last year, Oracle has significantly enhanced its solutions’ capabilities to help enterprises manage, analyze and remediate security incidents with Oracle’s autonomous security capabilities. We are continuing our commitment to offering a trusted identity fabric and portfolio to help enterprises secure their businesses.” 

According to Gartner, “IGA Leaders deliver a comprehensive toolset for governance and administration of identity and access. These vendors have successfully built a significant installed customer base and revenue stream, and have high viability ratings and robust revenue growth. Leaders also show evidence of superior vision and execution for anticipated requirements related to technology, methodology or means of delivery. Leaders typically demonstrate customer satisfaction with IGA capabilities and/or related service and support.” 

In December, Oracle announced the first cloud-native identity governance service for hybrid cloud environments, which will be fully integrated and native to Oracle’s SaaS applications, Oracle Identity Security Operations Center (Identity SOC) portfolio (including Oracle Identity Cloud Service and Oracle CASB Cloud Service), as well as Oracle Management Cloud. In addition, Oracle expanded its consumer identity management capabilities in Oracle Identity Cloud Service through integrations with Oracle Marketing Cloud and Oracle Data Cloud. 

Oracle’s integrated security suite of the Oracle Identity SOC portfolio and Oracle Management Cloud are designed to help enterprises forecast, reduce, detect, and resolve security threats and assist in efforts to remediate application and infrastructure performance issues. Leveraging artificial intelligence to analyze a unified data set consisting of the full breadth of security and operational telemetry, as well as provide automated remediation, Oracle’s integrated suite is designed to enable customers to quickly adapt their security and operational posture as their risk landscape changes. This application of machine learning can potentially help thwart attacks, reduce the detection window from months to minutes, and more quickly address security breaches and performance outages. 

Download a complimentary copy of Gartner’s 2018 “Magic Quadrant for Identity Governance and Administration” here

Source: Gartner, “Magic Quadrant for Identity Governance and Administration,” Felix Gaehtgens, Kevin Kampman, Brian Iverson, 21 February 2018. 

Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose. 

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Contact Info 

Jesse Caputo
Oracle
+1.650.506.5967

Oracle Hot Topics: INSTANCE CRASH ORA-07445 [KGGHSTFEL+192] ORA-07445[KGGHSTMAP+241]


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Larry Ellison: Oracle Is Revolutionizing The Database -- And IT Service Delivery


Saying it ranks among the “most important things we’ve ever done,” Oracle Executive Chairman and CTO Larry Ellison announced general availability of the company’s fully autonomous database service, Oracle Autonomous Data Warehouse Cloud.

It’s an entirely new class of IT offering, Ellison said, using machine learning, a branch of artificial intelligence (AI), to deliver industry-leading performance, security capabilities, and availability with no human intervention. “This technology changes everything,” he said. “The Oracle Autonomous Database is based on technology as revolutionary as the internet.”

By combining the high performance and high availability of the company’s Oracle Real Application Clusters (RAC) and Oracle Exadata technologies with machine learning, “the service can patch, tune, and update itself” with no human intervention, Ellison said. “The AI software learns kind of like kids learn,” he explained, “by inspecting large quantities of data and learning to understand patterns in what it encounters.”

To set up, provision, and use Oracle Autonomous Data Warehouse Cloud, a user simply answers a few short questions to determine how many CPUs and how much storage the data warehouse needs. Then the service configures itself typically in less than a minute and is ready to load data.

Once the data warehouse is up and running, its operation also is autonomous, delivering all of the analytic capabilities, security features, and high availability of Oracle Database without any of the complexities of configuration, tuning, and administration—even as warehousing workloads and data volumes change.

‘Really Impressed’

Early customers are seeing the benefits. “I've been really impressed with how Oracle Autonomous Data Warehouse Cloud can take different types and sizes of analytic workloads and get better performance without any fine-tuning on our side,” said Benjamin Arnulf, director of business intelligence and analytics with Hertz. “It will allow us to reduce costs and reinvest savings into improving customer service.” 

Sebastien Masson, a database administrator at CERN, the Swiss-based physics research center, noted that Oracle Autonomous Data Warehouse Cloud “automatically reduced the storage required by important control systems by a factor of 10.”

The data warehouse’s machine learning capabilities are also critical in the ongoing battle to secure data against cybercriminals and hostile nation states, Ellison said. In addition to encrypting all of its data, Oracle Autonomous Data Warehouse Cloud automatically applies patches to known system vulnerabilities while running.

“Having patching done autonomously is huge for closing those vulnerabilities,” said Paul Daugherty, chief technology and innovation officer at the global consulting firm, Accenture. “Then you can apply machine learning and get predictive to understand the threats and get smarter about how to diagnose and defend. That’s the future.”

Fulfilling its broader vision for Oracle Cloud Platform services, Oracle plans to roll out other autonomous services throughout 2018 and beyond, Ellison said, including autonomous integration, developer, mobile, Internet of Things, and online transaction processing services.

AWS Comes Up Short

At the launch event at company headquarters in Redwood City, California, Ellison showed how Oracle Autonomous Data Warehouse Cloud can run faster than comparable database offerings from Amazon Web Services, while being more scalable, and costing less.

Noting that Oracle charges the same per CPU minute as AWS, Ellison cited recently updated benchmarks that show workloads running in Oracle Autonomous Data Warehouse Cloud completed tasks five times faster (and thereby five times less expensively) than the same Oracle Database workloads running in the AWS cloud. “Same exact workload, same exact database,” he said.

In addition to running faster and thus costing less, Oracle Autonomous Data Warehouse Cloud is truly elastic, Ellison said, while the Amazon Elastic Compute Cloud, ironically, is not. With the AWS service, “you pay for a fixed configuration” and when you want to add CPUs, you have to take the database down and wait, he said.

George Lumpkin, an Oracle vice president of data warehousing, joined Ellison on stage to show how a business user would experience Oracle’s autonomous data warehouse. “If your workload goes up, the service increases CPUs to handle it,” Lumpkin said. “But let’s suppose you don’t need to use the data warehouse until tomorrow. Shut down the CPUs and you aren’t paying for them until you start them back up tomorrow.”

Oracle Autonomous Data Warehouse Cloud also self-tunes, continuously optimizing the system’s resources based on the queries coming in, which maintains high performance without any manual tuning or intervention by database administrators.

Oracle How Machine Learning Can Improve Recruiting


By Susan Poser, Oracle Insight, and Sharad Sinha, Oracle Strategy & Operations

How do you know you are hiring the right candidates? According to the Society for Human Resource Management, the average cost per hire is US$4,129, and it takes an average of 42 days to fill an open requisition. Imagine that amount being multiplied by all of your open positions over the course of the year. Then there is the issue on the other side of the coin—what if you rush to hire and you don’t hire the right person? A study found that 41% of employers estimated a single bad hire costs US$25,000, and 25% put the figure at US$50,000 or more.

What can companies do to reduce the cost per hire, as well as to reduce the chance of a bad hire? One approach is to use machine learning. Today, machine learning is already being used to make recruiting more efficient in three different areas: 
Application and résumé review: Screening résumés based on keywords, leveraging social data to identify candidates, and using online questionnaires 
  • Pre-engagement: Deploying artificial intelligence (AI) assistants and chatbots to respond to candidate inquiries or schedule interviews 
  • Talent sourcing: Narrowing top candidates from a large pool using key attributes 
How is machine learning doing this? Machine learning iteratively applies algorithmic analytical models to preprocessed data to uncover hidden patterns or trends that can be used to flag ideal résumés to review, predict the correct response to inquiries in the pre-engagement, or identify the best candidates for talent sourcing. While all of these areas can help reduce time and money spent to fill the position, there is one we believe can make the biggest impact to ensure you are hiring the right person: talent sourcing.

Will machines have better success finding the right candidates for your open positions than your recruiters? Ideally, a computer will find correlations and patterns that you would overlook, which would lead to increasingly higher-quality candidates. Here are some considerations if you are thinking about using machine learning to help with talent sourcing.

Passive Sourcing

To leverage machine learning, you need to first define the variables on which to “train” the system. The variables you should consider will depend on your approach. Are you sourcing passive candidates—people who aren’t actually looking for a new job—or are you looking to narrow top candidates from a large pool of applicants? If you are doing the former, you might want to consider attributes such as how recently or frequently they have updated their LinkedIn profile, because this could indicate that they might start looking for a job or are already on the hunt. Or consider factors affecting current employer stability (such as mergers and acquisitions, layoffs, and stock fluctuations). You could also look at market indicators to help predict a downturn in a particular industry or company, which might create a plethora of available candidates, giving you an early advantage.

Active Sourcing

On the active recruiting side, imagine receiving hundreds, if not thousands, of applications for open positions. Here is where machine learning can help narrow the top candidates, depending on the trainability of your data. Do you have enough historical and relevant data on successful candidates or employees to train your system? It’s as though you are looking for a “mini-me” based on the profile of the “ideal” employee. The attributes you consider here will depend on the role, but one approach would be to reverse-engineer the best fit by looking at the attributes of successful employees in that role, such as their work experience, industry, and work product. Other attributes to consider would be the number of jobs they’ve had in the last five years, their tenure in each job, major and extracurricular college activities for a recent-college-graduate hire, or hobbies (competitive sports might be good for a sales role). You could also leverage machine learning to target candidates who have a higher probability of success based on prior recruiting strategies.

Diversity and Inclusion

Some companies might be looking to fulfill a certain Equal Employment Opportunity ratio, and machine learning can help. One company used machine learning to help increase female hires from 40% to 47% and minority technical hires from 1.5% to 11%. However, in some cases, you don’t want to bias the “pool” based on gender or ethnicity. So how do you manage these situations? It is critical to eliminate the bias that could become inherent in machine learning. Data anonymity, clustering, and data aggregation are some ways to avoid the inherent biases (for example, ensuring that protected classes, gender, or age do not become factors in the algorithms).

Human Touch

While using machine learning can help reduce your recruiting cycle time, cost, and number of bad hires, you still need human intervention to manage the “candidate experience.” Humans will still need to ensure the candidate experience is positive through actions such as frequent personal communication and high-quality and consistent interviewing techniques, regardless of the outcome for the candidate.

The future of recruiting is certainly changing. The question is, are you ready? And if so, how will you embrace technology to help reduce hiring costs and increase your chances of hiring right the first time?

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miércoles, 4 de abril de 2018

#hackerdevil_oracle #hacking_etico Escalación de privilegios a DBA


Qué tan peligroso pueden ser estos 3 privilegios en conjunto?
@OracleDatabase

grant create session, create procedure, create any index to superusuario;

Mañana te lo contaremos.

Escalación de privilegios a DBA. Una razón más para migrar a 12c, si aún no lo haz hecho.
@oracleace

Optimismo para una vida Mejor

Optimismo para una vida Mejor
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