The field of AI is largely unexplored and we have only scratched the surface of what AI is really capable of.Understand the AI types will allow us to gain perspective on its capabilities and impacts in future.The degree to which an AI system can imitate human performance is used as a criterion for determining the type of AI.Based on this, AI is classified into two types-One type is based on classifying AI enabled machines based on their resemblance to the human mind , ability to think and feel.According to this system,the four types of AI are-reactive machines,limited theory machines,theory of mind and self aware AI.The second type of AI classification usually used in tech has three categories- Artificial Narrow Intelligence(ANI),Artificial General Intelligence (AGI), and Artificial Superintelligence (ASI). Read more at: https://cognitiveworld.com/articles/7-types-artificial-intelligence

The AI World Government,a three-day forum that started on June 24th, gathered leaders in the government,academic and industrial sectors to exchange views on the challenges and possible benefits of AI in the automation. According to leading companies such as Gartner and McKinsey,AI has cognitive abilities,and tends to imitate human performance by learning, understanding complexities, predicting and providing solutions.Intelligence and defence government agencies are heavily relying on AI for its application and growth in the areas of healthcare, transport, commerce and administration. It is clear that the government is investing heavily and wants to explore the capabilities of AI .The methodology of developing tools and applications of AI came across as a public-private partnership supported by investment which is looking at combining human and computer power.

Read more at: https://www.aitrends.com/ai-in-government/government-an-integral-partner-for-exploring-ai/

Big businesses have huge repositories of data.Each company is in the race to draw more value from this data so that they are not competed out.Data is unorganized,metadata initiatives have been falling and unearthing and extraction of data has become more difficult. Data protection regulations have exacerbated the problems.Lack of data management, renders data inefficient. IO Tahoe has published on Data Catalogs-a metadata management tool as a solution to this problem. The speed of AI coupled with human learning will help companies to overcome challenges of data management.There are several kinds of data catalogues and this report is a guide on which will be best for your company.

Read more at: https://insidebigdata.com/2019/06/18/ai-driven-data-catalogs-right-business/

A rare hereditary disease is difficult to diagnose. Sufferers have to go through innumerable tests,that take up valuable time of doctors and patients alike with still no definitive result. This additional time delays therapy that could actually be directed to forestall damage. Professors from the University Hospital Bonn and a team of researchers had data of 679 patients suffering from 105 different diseases .They trained a neural network model with 30,000 portrait pictures of affected people and demonstrated how artificial intelligence can be used to perform efficient and reliable facial analysis in cases where the facial structure does show mutations when afflicted with a rare disease. Together with other symptoms and genetic data,it was possible to get accurate diagnosis.

Read more at: https://www.sciencedaily.com/releases/2019/06/190606133805.htm

In 2014,NASA had lost an instrument located on the Solar Dynamics Observatory that measured UV rays coming from the sun,to forecast solar storms and to alleviate their affects. This is when scientists and engineers turned to artificial intelligence,with the thought that well-trained data can fill the data void. Four years of data captured by space instruments that included images of the sun were used to design processes.Using the best software tools to test these models,the scientists concluded that CNN is a good fit for the data giving 97.5% accuracy. Superior images of the sun generated were used to predict UV measurements.Our question stands at- if AI can be used to fill data gaps,can it forecast UV spectra as well and can it address a wider spectrum of problems?

Read more at: https://www.aitrends.com/neural-networks/how-ai-came-to-the-rescue-of-scientists-studying-the-sun/

With the advent of technology and analytics playing a major role,data storage has become of paramount importance.Just like every object in this universe occupies area,so does data. It just occupies space in the virtual paradigm. But there are problems with curating a data storage space. A well organized ,cost-effective long term data storage solution is what every company needs .High tech storage servers are required that absorb much of office space. It would mean spending on construction,on equipments and paying those who manage data. One can use cloud storage that takes advantage of other companies' infrastructure i.e. outsourcing data storage and maintaining responsibilities but this again would imply taking a risk with security. Storage space should be flexible so that it can be expanded in accordance with one's needs. Data should be accessible from different UIs and compatible with APIs.However, exposure to electromagnetic strips and waves may corrupt data.Data storage becomes futile,then. There are several problems with data storage that are yet be acknowledged and overcome.

Read more at: https://www.smartdatacollective.com/7-biggest-problems-data-storage-overcome/

A large number of tables with thousands of rows and columns. Data keeps flowing in from multiple live sources and is rapidly changing . This is what characterizes Big Data. Datasets are becoming vast and hence more complex for analysis. Without the correct tools,it would be difficult to use this data to draw insights. In this regard,three trends have come to the forefront- IoT,querying techniques and cloud computing.The advent of IoT has digitised everything. There are sensors and wires that chanel data which is then manipulated and analyzed to get results.Querying data i.e. extracting information from data is the first step before analysis and Cloud has come to play a major role in data storage. The three trends connected have paved a path for growth in technology.

Read more at: https://www.sisense.com/blog/waking-up-the-world-of-big-data/

AI is redefining the way oncologists look at cancer treatments.Deep learning has proven useful in understanding the trajectory of tumors in the distinctive case of cancers . According to two doctors,AI is making its mark in the healthcare industry.CNN(convoluted neural networks) have been trained to recognize and categorize cancers.CNN is used to diagnose tumor volume and segment tumor with higher precision. They have an advantage over semi-automatic methods,because the former can independently identify common features. Studying and interpretation of sample slides are slow and unreliable. DNN can be used to quicken the process and give robust results.A deep learning model teaches itself and becomes more accurate as it studies clinical data so it can predefine the form that a tumor will take.AI in healthcare has revolutionized diagnostic methods that aid better treatment.

Read more at: https://www.aiin.healthcare/topics/research/5-ways-ai-being-used-advance-cancer-research