30. October 2019 13:55
Artificial Intelligence (AI), Machine Learning and Deep Learning are commonly used interchangeably. However, in technological context; Machine Learning and Deep Learning are subsets of AI. In order to understand the difference between these terms, it is important to know the actual meaning of individual term.
Artificial Intelligence (AI): Artificial Intelligence is a term that defines the simulation of human intelligence processes by computer systems. The processes include learning, reasoning and self-correction. AI is broadly classified as weak (narrow) AI and strong AI. Weak AI systems are designed to do a particular task. The most common example of weak AI is the virtual personal assistants. On the contrary, strong AI systems are equipped with generalized human cognitive abilities. These systems are able to find a solution to any problem independent of human intervention.
Machine Learning (ML): ML is an application or subset of Artificial Intelligence. Under this application of AI, a machine is programmed to access and manipulate data. The machine can analyze the data to identify patterns and learn from these patterns. This allows the machine or computer system to modify decisions as per any change in data without explicit programming. Machine Learning is driven by algorithms and stat models. The common usage of Machine Learning can be found in apps such as email filtering, optimization, internet fraud detection, etc. Machine Learning methods are widely grouped as supervised and unsupervised ML.
- Unsupervised ML: These methods group interpret data based only on input data. Clustering methods are an example of unsupervised ML.
- Supervised ML: Supervised ML methods use both input and output data to develop a predictive model. Classification and Regression methods are listed as supervised ML.
Deep Learning (DL): It is a broader subset of AI. Deep Learning involves collection of large unstructured data and combing through it to generate classified structured information. The basic difference between Machine Learning and Deep Learning is that ML is task oriented learning, whereas DL is more general. It is used to derive meaning or identify patterns in unstructured data. This, in turn, helps in spotting large scale trends or irregularities. Some common applications of Deep Learning include self-driving cars, fraud news detection, natural language processing, visual recognition, etc.
For more information about Artificial Intelligence, Machine Learning and Deep Learning, call Centex Technologies at (972) 375-9654.
24. October 2019 15:25
18. October 2019 12:32
Most of the employees at workplace connect their mobile devices to secure corporate networks. The trend is gaining popularity as it offers flexibility and convenience. However, this has given rise to concerns over security, privacy and connectivity. With the rapid adoption of BYOD culture by organizations, there is a requirement for more dynamic security solutions. Without a MDM (mobile device management) software, business information on lost or stolen devices will not be secured and can lead to loss of data. Also, personal devices used by employees have increased exposure to malware and viruses that could compromise confidential data.
This results in a rise in number of incidents involving data breach and hacking. Such events are detrimental for a company’s reputation among customers and other business partners. As there is an increase in corporate cyber-attacks, businesses are seeing the value of comprehensive MDM solutions.
Mobile device management is a system that is designed for IT administrators to secure policies, permission rights and applications across multiple platforms. It enables easy monitoring of all mobile devices to safeguard all business applications and credential assets. The organizations, through MDM software, can have complete control over their data.
For effective results, MDM solutions should be executed effectively. Essential criteria for successful MDM solution are:
- Enforcement of security policies and passwords
- 24/7 monitoring and fully manageable
- Cloud-based system (to have automatic updates)
- Remote configuration and monitoring
- Restricting access to specific data and applications through Geo-fencing
- Remote data wiping to prevent unauthorized access
- Data restoration facility for corporate data
- Rooting alerts for any attempts to bypass restrictions
- Logging for compliance purposes
- Remote disabling of unauthorized devices
- Scalable – to accommodate new users and sophisticated devices
- Device troubleshooting
- Device location tracking
Other factors to be considered while implementing MDM solutions are:
- Architecture: MDM software should be implemented depending upon the preferences of an individual business. Even with the increase in cloud services and infrastructure; organizations still have some systems that are run in their own data centers. In this case, solutions are required for on-site, cloud and hybrid options.
- Direction: MDM solutions should be opted by a company depending upon the development of the enterprise. It should best fit current and future needs of the business.
- Integration: It is essential for MDM solutions to comply with the existing security and management controls of the business. The right software will enhance both security and efficiency, enabling IT administrators to monitor and control from a single access point.
For more information about Mobile Device Management, call Centex Technologies at (972) 375-9654.