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Wednesday 27 April 2016
The Foundations of Artificial Intelligence
In this post, we provide a brief history of the disciplines that contributed ideas, viewpoints, and techniques to AI. Like any history, this one is forced to (concentrate on a small number of people, events, and ideas and to ignore others that (also were important. We organize the history around a series of questions. We certainly would not wish to give the impression that these questions are the only ones the disciplines address or that the disciplines have all been working toward A1 as their ultimate fruition.
Philosophy
- Can formal rules be used to draw valid conclusions?
- How does the mental mind arise from a physical brain?
- Where does knowledge come from?
- How does knowledge lead to action?
Developments:
Aristotle (384 - 322 B.C.) was the first to formulate a precise set of laws governing the rational part of the mind. He developed an informal system of syllogisms for proper reasoning, which in principle allowed one to generate conclusions mechanically, given initial premises. Much later, Ramon Lull (d. 13 15) had the idea that useful reasoning could actually be carried out by a mechanical artifact. His " concept wheels " are on the cover of this book. Thomas Hobbes (1588 - 1679) proposed that reasoning was like numerical computation, that " we add and subtract in our silent thoughts. " The automation of computation itself was already well under way; around 1500, Leonardo da Vinci (1452 - 1519) designed but did not build a mechanical calculator; recent reconstructions have shown the design to be functional. The first known calculating machine was constructed around 1623 by the German scientist Wilhelm Schickard (1592-1635), although the Pascaline, built in 1642 by Blaise Pascal (1623-1662), is more famous. Pascal wrote that " the arithmetical machine produces effects which appear nearer to thought than all the actions of animals. " Gottfried Wilhelm Leibniz (1646 - 1716) built a mechanical device intended to carry out operations on concepts rather than numbers,
but its scope was rather limited.
Now that we have the idea of a set of rules that can describe the formal, rational part of the mind, the next step is to consider the mind as a physical system. RenC Descartes (1596 - 1650) gave the first clear discussion of the distinction between mind and matter and of the problems that arise. One problem with a purely physical conception of the mind is that it seems to leave little room for free will: if the mind is governed entirely by physical laws, then it has no more free will than a rock " deciding " to fall toward the center of the earth. Although a strong advocate of the power of reasoning, Descartes was also a proponent of dualism. He held that there is a part of the human mind (or soul or spirit) that is outside of nature, exempt from physical laws. Animals, on the other hand, did not possess this dual quality; they could be treated as machines. An alternative to dualism is materialism, which holds that the brain's operation according to the laws of physics constitutes the mind. Free will is simply the way that the perception of available choices appears to the choice process.
Given a physical mind that manipulates knowledge, the next problem is to establish the source of knowledge. The empiricism movement, starting with Francis Bacon's (1561 - 1626) Novum is characterized by a dictum of John Locke (1632 - 1704): " Nothing is in the understanding, which was not first in the senses. " David Hume's (171 1 - 1776) A Treatise of Human Nature (Hume, 1739) proposed what is now known as the principle of induction: that general rules are acquired by exposure to repeated associations between their elements. Building on the work of Ludwig Wittgenstein (1889 - 1951) and Bertrand Russell (1872-1970), the famous Vienna Circle, led by Rudolf Carnap (1891-1970), developed the doctrine of logical positivism. This doctrine holds that all knowledge can be characterized by logical theories connected, ultimately, to observation sentences that correspond to sensory inputs. The confirmation theory of Carnap and Carl Hempel (1905 - 1997) attempted to understand how knowledge can be acquired from experience. Carnap's book The Logical Structure of the World (1928) defined an explicit computational procedure for extracting knowledge from elementary experiences. It was probably the first theory of mind as a computational process.
The final element in the philosophical picture of the mind is the connection between knowledge and action. This question is vital to AI, because intelligence requires action as well as reasoning. Moreover, only by understanding how actions are justified can we understand how to build an agent whose actions are justifiable (or rational). Aristotle argued that actions are justified by a logical connection between goals and knowledge of the action's outcome (the last part of this extract also appears on the front cover of this book).
Thinking humanly: The cognitive modeling approach
If we are going to say that a given program thinks like a human, we must have some way of determining how humans think. We need to get inside the actual workings of human minds. There are two ways to do this: through introspection trying to catch our own thoughts as they go by and through psychological experiments. Once we have a sufficiently precise theory of the mind, it becomes possible to express the theory as a computer program. If the program's input/output and timing behaviors match corresponding human behaviors, that is evidence that some of the program's mechanisms could also be operating in humans. For example, Allen Newell and Herbert Simon, who developed GPS, the " General Problem Solver "
(Newell and Simon, 1961), were not content to have their program solve problems correctly. They were more concerned with comparing the trace of its reasoning steps to traces of human subjects solving the same problems. The interdisciplinary field of cognitive science brings together computer models from AI and experimental techniques from psychology to try to construct precise and testable theories of the workings of the human mind.
Cognitive science is a fascinating field, worthy of an encyclopedia in itself (Wilson and Keil, 1999). We will not attempt to describe what is known of human cognition in this book. We will occasionally comment on similarities or differences between AI techniques and human cognition. Real cognitive science, however, is necessarily based on experimental investigation of actual humans or animals, and we assume that the reader has access only to a computer for experimentation.
In the early days of AI there was often confusion between the approaches: an author would argue that an algorithm performs well on a task and that it is therefore a good model of human performance, or vice versa. Modern authors separate the two kinds of claims; this distinction has allowed both AI and cognitive science to develop more rapidly. The two fields continue to fertilize each other, especially in the areas of vision and natural language. Vision in particular has recently made advances via an integrated approach that considers neurophysiological evidence and computational models.
Thinking rationally: The " laws of thought " approach
The Greek philosopher Aristotle was one of the first to attempt to codify " right thinking, " that is, irrefutable reasoning processes. His syllogisms provided patterns for argument structures that always yielded correct conclusions when given correct premises - for example, " Socrates is a man; all men are mortal; therefore, Socrates is mortal. " These laws of thought were supposed to govern the operation of the mind; their study initiated the field called logic.
Logicians in the 19th century developed a precise notation for statements about all kinds of things in the world and about the relations among them. (Contrast this with ordinary arithmetic notation, which provides mainly for equality and inequality statements about numbers.) By 1965, programs existed that could, in principle, solve any solvable problem described in logical notation. The so - called logicist tradition within artificial intelligence hopes to build on such programs to create intelligent systems.
There are two main obstacles to this approach. First, it is not easy to take informal knowledge and state it in the formal terms required by logical notation, particularly when the knowledge is less than 100% certain. Second, there is a big difference between being able to solve a problem " in principle " and doing so in practice. Even problems with just a few dozen facts can exhaust the computational resources of any computer unless it has some guidance as to which reasoning steps to try first. Although both of these obstacles apply to any attempt to build computational reasoning systems, they appeared first in the logicist tradition.
Acting rationally: The rational agent approach
An agent is just something that acts (agent comes from the Latin agere, to do). But computer
agents are expected to have other attributes that distinguish them from mere " programs, "
such as operating under autonomous control, perceiving their environment, persisting over a
prolonged time period, adapting to change, and being capable of taking on another's goals.
RATIONAL AGENT:
A rational agent is one that acts so as to achieve the best outcome or, when there is uncertainty, the best expected outcome.
In the " laws of thought " approach to AI, the emphasis was on correct inferences. Making correct inferences is sometimes part of being a rational agent, because one way to act rationally is to reason logically to the conclusion that a given action will achieve ones goals and then to act on that conclusion. On the other hand, correct inference is not all of rationality, because there are often situations where there is no provably correct thing to do, yet something must still be done. There are also ways of acting rationally that cannot be said to involve inference. For example, recoiling from a hot stove is a reflex action that is usually more successful than a slower action taken after careful deliberation.
All the skills needed for the Turing Test are there to allow rational actions. Thus, we need the ability to represent knowledge and reason \with it because this enables us to reach good decisions in a wide variety of situations. We need to be able to generate comprehensible sentences in natural language because saying those sentences helps us get by in a complex society. We need learning not just for erudition, but because having a better idea of how the world works enables us to generate more effective strategies for dealing with it. We need visual perception not just because seeing is fun, but to get a better idea of what an action might achieve for example, being able to see a tasty morsel helps one to move toward it.
For these reasons, the study of AI as rational agent design has at least two advantages. First, it is more general than the " laws of thought " approach, because correct inference is just one of several possible mechanisms for achieving rationality. Second, it is more amenable to scientific development than are approaches based on human behavior or human thought because the standard of rationality is clearly defined and completely general. Human behavior, on the other hand, is well - adapted for one specific environment and is the product, in part, of a complicated and largely unknown evolutionary process that still is far from producing perfection. This book will therefore concentrate on general principles of rational agents and on components for constructing them. We will see that despite the apparent simplicity with which the problem can be stated, an enormous variety of issues come up when we try to solve it.
What is Artificial Intelligence ?
We have
claimed that AI is exciting, but we have not said what
it is?
Definitions
of artificial intelligence according to four basic approaches to define AI.
These approaches are vary two major dimensions first, thought processes &
reasoning.There are four basic approaches to define AI:
Systems
that think like humans
Systems
that think rationally
Systems
that act like humans
Systems
that act rationally
System
that think like humans
The first
approach define that" The exciting new effort to make computers think
& machines with minds, in the full
and literal sense. This approach appeared in 1985 by Haugeland. In second
"The automation of activities that we associate with human thinking,
activities such as decision making,
problem solving, learning.( A person who gave this approach concept, named was
Bellman, in 1978).The question was raised in every mind that was: Can system will
do it? Can system think like humans?
Systems
that think rationally
In second
approach define that" The study of mental faculties through the use of
computational models. "(This approach was given by Chamiak and McDermott,
in 1985). In second " The study of the computations that make it possible
to perceive, reason, and act. "(This concept was given by Winston , in
1992).
Systems
that act like humans
This
approach was different from others, because in that approach the AI researchers
said that Computer can act like humans like think, speak and learn. We will
take two major concepts and judge can possible that systems or computers act
like humans. In first concept " The art of creating machines that perform
functions that require intelligence when performed by people. " (This concept was given by Kurzweil, in
1990). In second concept " The study of how to make computers do things at
which, at the moment, people are better. "(this concept was given by Rich
and Knight, in 1991).
Systems
that act rationally
In fourth
approach " Computational Intelligence is the study of the design of
intelligent agents. " (Poole
et al gave this concept in 1998) In
second concept of this approach was "A1
. . .is concerned with intelligent behavior in
artifacts. " (this concept was
given by Nilsson in 1998).
Historically,
all four approaches to A1 have been followed. As one might expect, a tension exists between approaches centered
around humans and approaches centered around rationality.
A human - centered approach must be an
empirical science, involving hypothesis and experimental confirmation. A
rationalist approach involves a combination of mathematics and
engineering. Each group has both disparaged and helped the other. Let us look
at the four approaches in more detail.
Acting
humanly: The Turing Test approach
The Turing Test,
proposed by Alan Turing in 1950, this test was designed to provide a
satisfactory operational definition of intelligence. Rather than proposing a
long and perhaps controversial list of qualifications required for
intelligence, he suggested a test based on indistinguishably from undeniably
intelligent entities human beings. The computer passes the test if a human
interrogator, after posing some written questions, cannot tell whether the
written responses come from a person or not. Further discusses the details of
the test and whether a computer is really intelligent if it passes. For now, we
note that programming a computer to pass the test provides plenty to work on.
The computer would need to possess the following capabilities:
Natural
language processing to enable it to
communicate successfully in English.
Knowledge
representation to store what it knows or
hears.
Automated
reasoning to use the stored information
to answer questions and to draw new conclusions.
Machine learning to adapt to new circumstances and to detect
and extrapolate patterns.
Turing's
test deliberately avoided direct physical interaction between the interrogator
and the computer, because physical simulation of a person is unnecessary for
intelligence. However, the so called
total Turing Test includes a
video signal so that the interrogator can test the subject's perceptual
abilities, as well as the opportunity for the interrogator to pass physical
objects " through the hatch.
" To pass the total Turing Test,
the computer will need
Computer
vision to perceive objects & robotics to manipulate objects and move about.
These six
disciplines compose most of AI, and Turing deserves credit for designing a test
that remains relevant 50 years later. Yet A1 researchers have devoted
little effort to passing the Turing test, believing that it is more important
to study the underlying principles of intelligence than to duplicate an
exemplar. The quest for "artificial flight " succeeded when the Wright brothers and others
stopped imitating birds and learned about aerodynamics. Aeronautical
engineering texts do not define the goal of their field as making " machines that fly so exactly like
pigeons that they can fool even other pigeons.
Tuesday 26 April 2016
Buzzwords
No discussion of the genesis of
Java is complete without a look at the Java buzzwords. Although the fundamental
forces that necessitated the invention of Java are portability and security,
other factors also played an important role in molding the final form of the
language. The key considerations were summed up by the Java team in the following
list of buzzwords:
Simple
Java was designed to be easy for
the professional programmer to learn and use effectively. Assuming that you
have some programming experience, you will not find Java hard to master. If you
already understand the basic concepts of object-oriented programming, learning
Java will be even easier. Best of all, if you are an experienced C++
programmer, moving to Java will require very little effort. Because Java
inherits the C/C++ syntax and many of the object-oriented features of C++, most
programmers have little trouble learning Java. Also, some of the more confusing
concepts from C++ are either left out of Java or implemented in a cleaner, more
approachable manner. Beyond its similarities with C/C++, Java has another
attribute that makes it easy to learn: it makes an effort not to have
surprising features. In Java, there are a small number of clearly defined ways
to accomplish a given task.
Object-Oriented
Although influenced by its
predecessors, Java was not designed to be source-code compatible with any other
language. This allowed the Java team the freedom to design with a blank slate.
One outcome of this was a clean, usable, pragmatic approach to objects.
Borrowing liberally from many seminal object-software environments of the last
few decades, Java manages to strike a balance between the purist’s “everything
is an object” paradigm and the pragmatist’s “stay out of my way” model. The
object model in Java is simple and easy to extend, while simple types, such as
integers, are kept as high-performance non-objects.
Robust
The multiplatformed environment of
the Web places extraordinary demands on a program, because the program must
execute reliably in a variety of systems. Thus, the ability to create robust
programs was given a high priority in the design of Java. To gain reliability,
Java restricts you in a few key areas, to force you to find your mistakes early
in program development. At the same time, Java frees you from having to worry about
many of the most common causes of programming errors. Because Java is a strictly
typed language, it checks your code at compile time. However, it also checks your
code at run time. In fact, many hard-to-track-down bugs that often turn up in hard-to-reproduce
run-time situations are simply impossible to create in Java. Knowing that what
you have written will behave in a predictable way under diverse conditions is a
key feature of Java. To better understand how Java is robust, consider two of
the main reasons for program failure: memory management mistakes and mishandled
exceptional conditions (that is, run-time errors). Memory management can be a
difficult, tedious task in traditional programming environments. For example,
in C/C++, the programmer must manually allocate and free all dynamic memory.
This sometimes leads to problems, because programmers will either forget to
free memory that has been previously allocated or, worse, try to free some memory
that another part of their code is still using. Java virtually eliminates these
problems by managing memory allocation and deallocation for you. (In fact,
deallocation is completely automatic, because Java provides garbage collection
for unused objects.) Exceptional conditions in traditional environments often
arise in situations such as division by zero or “file not found,” and they must
be managed with clumsy and hard-to-read constructs. Java helps in this area by
providing object-oriented exception handling. In a well-written Java program,
all run-time errors can be managed by your program.
Multithreaded
Java was designed to meet the
real-world requirement of creating interactive, networked programs. To
accomplish this, Java supports multithreaded programming, which allows you to
write programs that do many things simultaneously. The Java run-time system
comes with an elegant yet sophisticated solution for multi-process synchronization
that enables you to construct smoothly running interactive systems. Java’s
easy-to-use approach to multithreading allows you to think about the specific behavior
of your program, not the multitasking subsystem.
Architecture-Neutral
A central issue for the Java
designers was that of code longevity and portability. One of the main problems
facing programmers is that no guarantee exists that if you write a program
today, it will run tomorrow even on the same machine. Operating system upgrades,
processor upgrades, and changes in core system resources can all combine to
make a program malfunction. The Java designers made several hard decisions in
the Java language and the Java Virtual Machine in an attempt to alter this
situation. Their goal was “write once; run anywhere, anytime, forever.” To a
great extent, this goal was accomplished.
Interpreted and High Performance
As described earlier, Java enables
the creation of cross-platform programs by compiling into an intermediate
representation called Java bytecode. This code can be interpreted on any system
that provides a Java Virtual Machine. Most previous attempts at cross-platform solutions
have done so at the expense of performance. Other interpreted systems, such as
BASIC, Tcl, and PERL, suffer from almost insurmountable performance deficits.
Java, however, was designed to perform well on very low-power CPUs. As explained
earlier, while it is true that Java was engineered for interpretation, the Java
bytecode was carefully designed so that it would be easy to translate directly
into native machine code for very high performance by using a just-in-time
compiler. Java run-time systems that provide this feature lose none of the
benefits of the platform-independent code. “High-performance cross-platform” is
no longer an oxymoron.
Distributed
Java is designed for the
distributed environment of the Internet, because it handles TCP/IP protocols.
In fact, accessing a resource using a URL is not much different from accessing
a file. The original version of Java (Oak) included features for intra-address-space
messaging. This allowed objects on two different computers to execute procedures
remotely. Java revived these interfaces in a package called Remote Method Invocation
(RMI). This feature brings an unparalleled level of abstraction to client/ server
programming.
Dynamic
Java programs carry with them
substantial amounts of run-time type information that is used to verify and
resolve accesses to objects at run time. This makes it possible to dynamically
link code in a safe and expedient manner. This is crucial to the robustness of
the applet environment, in which small fragments of bytecode may be dynamically
updated on a running system.
Best Fitness Plan (Part 2)
THE SHRED-DOWN TRAINING PLAN
Over the course of the next 21 days, you will be exercising almost every day, with only a few days of rest. The program will begin (Days 1–7) by attacking the entire body with full-body, fast paced workouts every other day with brief interval workouts, ab circuits, and longer duration, low-intensity cardio being alternated for non-lifting days. The next phase will include traditional, old school, bodybuilding-style splits (Days 9–14). The final phase of the program will revert back to the original full-body workouts, but with an increased work capacity. You will have a total of three days “off” during the program. Days 8, 11, and 13 will serve as complete recovery days.
Days 6 and 17 will consist of a unique fat-burning and endurance/conditioning challenge.
KICKING INTO GEAR IN THE AM:
The Morning Grinder Workouts
The key to melting off fat is being active as much as possible, and these workouts produce short bursts of moderate to intense work that will help speed up the fat burning process. It’s true, 21 days is not a lot of time, but it’s doable with some extra effort, and these Morning Grinder sessions are brief, effective ways to jump-start your metabolism each day. Here are three sample workouts; switch and swap them, but do one every morning in a fasted state i.e., before breakfast. Is it a pain to have to work out every morning? Yes, but if you want it bad enough, you’ll do it.
GRINDER 1JUMP ROPE – 1 MINUTE
BODY-WEIGHT SQUAT – 30 SECONDS
PLANK – 1 MINUTE
MOUNTAIN CLIMBER – 30 SECONDS
REST – 30 SECONDS
COMPLETE 3 SETS
GRINDER 2
MEDICINE BALL RUSSIAN TWIST – 30 REPS
> SUPER-SET WITH: MEDICINE BALL V-UP – AS MANY AS POSSIBLE
REST – 30 SECONDS
COMPLETE 4 SETS
GRINDER 3
DUMBBELL PUSH-UP WITH ROW – 15 REPS
> SUPER-SET WITH: PRISONER SQUAT – 15 REPS
REST – 30 SECONDS
COMPLETE 3 SETS
TRAINING BENCHMARKS:
Power Hours
One of the most popular workout series’ on the Men’s Fitness social media streams are the Power Hour sessions. On Day 6, you will complete a salablez Jump Rope and Run workout measured by time. On Day 17 you will complete another easily adjustable workout referred to as a Muscle Run. This workout will consist of running and various body-weight exercises at checkpoints.
DAY 1 Full Body
Full-body workouts sound like they would take a long time. But when you boil down the exercises you need to perform in order to cover every area, there are only three types you need to be concerned with a push, a pull, and a squat. This is the ultimate in minimalism and works superbly for beginners or people who are short on time.
HOW IT WORKS Any kind of pressing exercise will train your chest, shoulders, and triceps. Any pulling movement (a row or chinup variation) recruits your back, rear delts, biceps, and forearms. Squatting movements (and deadlifts, which aren’t quite a squat but require all the same muscles) take care of the quads, hamstrings, and glutes. Even your calves get some stimulation as they help to stabilize your squat. Your abs, of course, get worked on all these movement patterns, provided they’re done with free weights rather than machines; also work to brace your spine throughout. The following workout, done with heavy weights, contains everything you need to put on size fast a squat, press, and pullup and you should be able to wrap it up within 45 minutes.
DIRECTIONS Complete all five sets for the squat and then perform the overhead press and weighted pullup in alternating fashion. That is, complete a set of the press, rest, then do a set of the pull-up, rest again, and repeat until you’ve finished all five sets for each.
The Byte-Code in Java
The key that allows Java
to solve both the security and the portability problems just described is that
the output of a Java compiler is not executable code. Rather, it is bytecode.
Bytecode is a highly optimized set of instructions designed to be executed by
the Java run-time system, which is called the Java Virtual Machine (JVM). That
is, in its standard form, the JVM is an interpreter for bytecode. This may come
as a bit of a surprise. As you know, C++ is compiled to executable code. In
fact, most modern languages are designed to be compiled, not interpreted—mostly
because of performance concerns. However, the fact that a Java program is
executed by the JVM helps solve the major problems associated with downloading
programs over the Internet. Here is why.
Translating a Java
program into bytecode helps makes it much easier to run a program in a wide
variety of environments. The reason is straightforward: only the JVM needs to
be implemented for each platform. Once the run-time package exists for a given
system, any Java program can run on it. Remember, although the details of the
JVM will differ from platform to platform, all interpret the same Java
bytecode. If a Java program were compiled to native code, then different versions
of the same program would have to exist for each type of CPU connected to the
Internet. This is, of course, not a feasible solution. Thus, the interpretation
of bytecode is the easiest way to create truly portable programs.
The fact that a Java
program is interpreted also helps to make it secure. Because the execution of
every Java program is under the control of the JVM, the JVM can contain the
program and prevent it from generating side effects outside of the system. As
you will see, safety is also enhanced by certain restrictions that exist in the
Java language. When a program is interpreted, it generally runs substantially
slower than it would run if compiled to executable code. However, with Java,
the differential between the two is not so great. The use of bytecode enables
the Java run-time system to execute programs much faster than you might expect.
Although Java was
designed for interpretation, there is technically nothing about Java that
prevents on-the-fly compilation of bytecode into native code. Along these lines,
Sun supplies its Just In Time (JIT) compiler for bytecode, which is included in
the Java 2 release. When the JIT compiler is part of the JVM, it compiles
bytecode into executable code in real time, on a piece-by-piece, demand basis. It
is important to understand that it is not possible to compile an entire Java
program into executable code all at once, because Java performs various run-time
checks that can be done only at run time. Instead, the JIT compiles code as it
is needed, during execution. However, the just-in-time approach still yields a
significant performance boost. Even when dynamic compilation is applied to
bytecode, the portability and safety features still apply, because the run-time
system (which performs the compilation) still is in charge of the execution
environment. Whether your Java program is actually interpreted in the traditional
way or compiled on-the-fly, its functionality is the same.
Best Fitness Plan (Part 1 EAT)
Introduction:
You need only three weeks for better results. In three weeks basic six parts will be completed.Part 1: Eat
Part 2: Train
Part 3: Supplement
Part 4: Challenge
Part 5: FEAST
Part 6: Thrive
We will discuss each part in one Post. Now I am going to discuss Part 1 and that is Eat.
Part 1: Eat
The only way you will ever lose fat, shred your physique, and see your abs is if you eat fewer calories than you are eating now. Every day for the next three weeks you will be either training or recovering from training. That means that a conscious balance of protein, fat, fiber rather than a wholesale carbohydrate fast is the best bet. Nothing else not lifting weights, not supplements, not sitting in a sauna wearing a polyester burka can offset failing to adhere to this simple rule. Period. So
for the next 21 days.
IT COMES DOWN TO CALORIES
According to Mayo Clinic research, a 160-pound person performing high-impact aerobic exercise will burn only 533 calories in one hour. (Note that most people are not capable of sustaining an intense pace anywhere near that long.) Now consider that a healthy dinner of just four ounces of skinless chicken breast and one cup of rice contains 385 calories. That’s right: Eat one light meal and you are a stone’s throw from breaking even with the calories you burned in that day’s workout assuming the workout was long and extremely vigorous in the first place. So if you can not create a caloric deficit with exercise (at least not without a ton of it, marathon runners notwithstanding) you must do it with your diet. So let’s get started.
First, multiply your current body weight by 12 that’s roughly how many calories you should consume per day in order to maintain your current weight. Now do the same calculation using the body weight you are aiming for. So if you are 220 pounds but remember looking and feeling your best when you were 190, start taking in 2,300 calories a day (190 × 12, rounded up for simplicity). On the other hand, if you’re 185 and only want to strip off five to 10 extra pounds, eating 2,160 calories per day (180 × 12) combined with the serious fitness routine, will get you there in record time. (By the way: This sort of eating plan, absent strenuous exercise, will get you to your desired weight over time.
But combined with the workout plan in these pages, eating this way will strip fat off your body with amazing speed while ensuring you have enough of the right kind of calories to maintain muscle growth. Again, it’s all about the balance between burning and building.)
Understand that these numbers are just a starting point. They should allow you to lose one or two pounds per week initially (more if you are heavier) but also build dense, heavy muscle at the same time. So worry less about the scale than about how you look and feel. If your weight loss stops dead for more than one week, cut your calories and readjust your numbers. Instead of 12 calories per pound, try dropping to 11 calories per pound first and then, later, 10. But don’t go any lower: Losing more than a few pounds a week means you’re giving up muscle mass as well as fat. That’s not shredding; that’s yo-yo dieting, and it will set you up for greater weight gain down the road.
THE 8 ABSOLUTE-WORST FOODS YOU
CAN PUMP INTO YOUR BODY
Packed with calories and nearly devoid of any real nutrition, these 8 foods are not only completely off limits for the 21-Day Shred, but they should be removed from your kitchen, fridge, and cabinets forever.
2: BAGELS
3: PROCESSED CEREAL
4: CHIPS
5: FRENCH FRIES
6: FAST-FOOD BURGERS
7: MICROWAVE POPCORN
8: MARGARINE
MIND YOUR MACROS
Of course, 2,160 calories of Doritos is not going to sculpt the body you want. To maximize results from your weight training, you need to eat the right combination of macro nutrients. (Macro nutrients means protein, carbohydrates, and fat; micro nutrients refers to things like vitamins and minerals.)
Protein is the building block of muscle, and it will be the cornerstone of your diet plan. Every day, regardless of training or not, you’ll shoot for 1–1.25 grams per pound of body weight it will be critical for recovery and building new muscle.
For carbohydrates , you have to get a bit more strategic. On strength-training days you are going to consume approximately between 0.5 and 0.75 grams of carbohydrates per pound of body weight. But this will be distributed at key points during the day: some in the mid morning time frame, but primarily in post-workout portions of your day to optimist recovery from your workout.
On interval/cardio/ab days, your crab intake will hover around only 0.25 grams. On rest days, your body won't need much in the way of curbs, so you will reduce your intake to less than 0.25 grams. Fat , although clerically dense, plays a crucial role in supporting the production of hormones such as testosterone, so while it must be kept fairly low to help create the caloric deficit we are aiming for, you can not cut it out completely. Get 0.3 grams of fat per pound of your target body weight so a 175-pound target means consuming about 52 grams daily. Most of your fat intake should come as a by-product of the protein-rich foods you eat.
STAGGER SIX MEALS A DAY
Being on a hardcore shred program is going to make you hungry. You are going to want to eat and eat and eat. That’s why this program calls for six meals a day, to keep you from making hunger-driven food mistakes while also feeding your body the nutrients it needs to recover and perform.
Spotlight on Post-Workout:
After hitting the weights hard, your body’s levels of cortisol a stress-induced hormone that triggers fat storage are on the rise, and that can actually be harmful to muscle growth if it’s not addressed. That’s why eating right after exercise and making sure you get enough post-workout carbs is so important. Approximately 30 minutes or so will be your window of opportunity to recover appropriately. A meal combining both protein and curbs will suppress cortisol, and immediately begin the muscle-growth and recovery process. Avoid fats immediately post-workout because they slow digestion and can interfere/slow the effects of post-workout protein/crab combo.
NOTE: This plan is designed for an evening (6 p.m. or so) workout schedule. If you work out earlier, move the post-workout meal (currently Meal 5 in this schedule) so they happen right after your exercise session.
STRENGTH-TRAINING DAYS
(Evening workouts)
MEAL 1
Time 7:30 a.m.
- 4 egg whites:
- 70 calories,
- 14 g protein,
- 0.5 g carbs,
- 0.5 g fat
- 2 whole eggs:
- 145 calories,
- 15 g protein,
- 0.5 g carbs,
- 9 g fat
- ½ cup oatmeal:
- 120 calories,
- 1 g protein,
- 25 g carbs,
- 1 g fat
MEAL 2
Time 10:30 a.m.
- 1 skinless chicken breast, halved:
- 260 calories,
- 56 g protein,
- 0 g carbs,
- 1.5 g fat
- 1 cup broccoli, cooked:
- 34 calories,
- 2 g protein,
- 7 g carbs (fiber),
- 0 g fat
MEAL 3
Time 1:30 p.m.
- 1 skinless chicken breast, halved:
- 260 calories,
- 56 g protein,
- 0 g carbs,
- 1.5 g fat
- ½ cup mixed veggies:
- 60 calories,
- 3 g protein,
- 11 g carbs,
- 0 g fat
SMALL SNACK
Time 4 p.m.
- 1 tbsp all natural peanut butter:
- 100 calories,
- 4 g protein,
- 3 g carbs,
- 7 g fat
- + celery sticks
OR
- handful almonds + celery sticks
MEAL 4
Time 5:30 p.m.
- 6 oz ahi tuna steak:
- 180 calories,
- 40 g protein,
- 0 g carbs,
- 1.5 g fat
- ½ cup oatmeal:
- 120 calories,
- 1 g protein,
- 25 g carbs,
- 1 g fat
MEAL 5
- Post workout meal: 7:30 p.m.
- Blend together with ice:
- 1¼ scoops whey protein:
- 150 calories,
- 30 g protein,
- 2 g carbs,
- 2 g fat
- ½ cup oatmeal:
- 120 calories,
- 1 g protein,
- 25 g carbs,
- 1 g fat
- 1 medium banana:
- 105 calories,
- 1 g protein,
- 26 g carbs,
- 5 g fat
MEAL 6
Time 9:30 p.m.
- 1 skinless chicken breast, halved:
- 260 calories,
- 56 g protein,
- 0 g carbs,
- 1.5 g fat
- 1 cup broccoli, cooked:
- 34 calories,
- 2 g protein,
- 7 g carbs (fiber),
- 0 g fat
- ½ avocado:
- 130 calories,
- 1 g protein,
- 6 g carbs,
- 12 g of fat
TOTALS
- Calories: 2,148
- Protein: 222
- Carbs: 124
- Fat: 40
INTERVAL TRAINING DAYS
(Evening workouts)
MEAL 1
Time 7:30 a.m.
- 4 egg whites:
- 70 calories,
- 14 g protein,
- 0.5 g carbs,
- 0.5 g fat
- 2 whole eggs:
- 145 calories,
- 15 g protein,
- 0.5 g carbs,
- 9 g fat
- ½ cup oatmeal:
- 120 calories,
- 1 g protein,
- 25 g carbs,
- 1 g fat
MEAL 2
Time 10:30 a.m.
- 1 skinless chicken breast, halved:
- 260 calories,
- 56 g protein,
- 0 g carbs,
- 1.5 g fat
- 1 cup broccoli, cooked:
- 34 calories,
- 2 g protein,
- 7 g carbs (fiber),
- 0 g fat
MEAL 3
- 1:30 p.m.
- 1 skinless chicken breast, halved:
- 260 calories,
- 56 g protein,
- 0 g carbs,
- 1.5 g fat
- ½ cup mixed veggies:
- 60 calories,
- 3 g protein,
- 11 g carbs,
- 0 g fat
SMALL SNACK
Time 4 p.m.
- 1 tbsp all natural peanut butter:
- 100 calories,
- 4 g protein,
- 3 g carbs,
- 7 g fat
- + celery sticks
OR
- handful almonds + celery sticks
MEAL 4
Time 6 p.m.
- 6 oz ahi tuna steak:
- 180 calories,
- 40 g protein,
- 0 g carbs,
- 1.5 g fat
MEAL 5
- Post workout meal: 7:00/7:30PM
- Blend together with ice:
- 1¼ scoops whey protein:
- 150 calories,
- 30 g protein,
- 2 g carbs,
- 2 g fat
- ½ cup oatmeal:
- 120 calories,
- 1 g protein,
- 25 g carbs,
- 1 g fat
MEAL 6
Time 9:30 p.m.
- 1 skinless chicken breast, halved:
- 260 calories,
- 56 g protein,
- 0 g carbs,
- 1.5 g fat
- 1 cup broccoli, cooked:
- 34 calories,
- 2 g protein,
- 7 g carbs (fiber),
- 0 g fat
- 1 avocado:
- 260 calories,
- 2 g protein,
- 12 g carbs,
- 24 g fat
TOTALS
- Calories: 2,053
- Protein: 218
- Carbs: 75
- Fat: 52
REST DAYS
MEAL 1
Time 7:30 a.m.
- 4 egg whites:
- 70 calories,
- 14 g protein,
- 0.5 g carbs,
- 0.5 g fat
- 2 whole eggs:
- 145 calories,
- 15 g protein,
- 0.5 g carbs,
- 9 g fat
- ¼ cup oatmeal:
- 60 calories,
- 0.5 g protein,
- 13 g carbs,
- 0.5 g fat
MEAL 2
Time 10:30 a.m.
- 1 skinless chicken breast, halved:
- 260 calories,
- 56 g protein,
- 0 g carbs,
- 1.5 g fat
- 1 cup broccoli, cooked:
- 34 calories,
- 2 g protein,
- 7 g carbs (fiber),
- 0 g fat
MEAL 3
Time 1:30 p.m.
- 1 skinless chicken breast, halved:
- 260 calories,
- 56 g protein,
- 0 g carbs,
- 1.5 g fat
- ½ cup mixed veggies:
- 60 calories,
- 3 g protein,
- 11 g carbs,
- 0 g fat
- SMALL SNACK
- 4 p.m.
- 1 tbsp all natural peanut butter:
- 100 calories,
- 4 g protein,
- 3 g carbs,
- 7 g fat
- + celery sticks
OR
- handful almonds + celery sticks
MEAL 4
Time 6 p.m.
- 6 oz ahi tuna steak:
- 180 calories,
- 40 g protein,
- 0 g carbs,
- 1.5 g fat
- ½ cup oatmeal:
- 120 calories,
- 1 g protein,
- 25 g carbs,
- 1 g fat
MEAL 5
Time 7:00/7:30 p.m.
- Mix with with ice and a splash of almond milk:
- 1¼ scoops whey protein:
- 150 calories,
- 30 g protein,
- 2 g carbs,
- 2 g fat
MEAL 6
Time 9:30 p.m.
- 1 skinless chicken breast, halved:
- 260 calories,
- 56 g protein,
- 0 g carbs,
- 1.5 g fat
- 1 cup broccoli, cooked:
- 34 calories,
- 2 g protein,
- 7 g carbs (fiber),
- 0 g fat
- 1 avocado:
- 260 calories,
- 2 g protein,
- 12 g carbs,
- 24 g fat
TOTALS:
- Calories: 1,863
- Protein: 216
- Carbs: 65
- Fat: 52
8 FOODS THAT WILL NEVER MAKE YOU FAT
Strict dieting and tons of sweating means strong food cravings. If you’ve gotta eat, dammit, and mealtime is too far out, reach for one of these fast, nearly zero-calorie options and eat as much as you’d like.
1: CELERY
Calories per cup, chopped: 16
Celery is one of the best “negative- calorie” foods. And although it still contains 10 calories per large stalk, the majority of that is coming from small amounts of fiber and hints of sugar. Either way, go ahead and eat up as much as you’d like. It will keep you feeling full and won’t impact your daily macronutrient limits.
2: PEPPERS
Calories per medium pepper: 30
Red peppers are ripe, which means they pack several times as many nutrients as unripe green peppers.
3: KALE
Calories per cup, chopped: 33
Kale has found its way onto every healthy food list, and it would be wrong to leave it off of this one. Kale is one of the best sources of folate, an essential B vitamin.
4: BROCCOLI
Calories per cup, chopped: 31
Cruciferous vegetables are the mainstay when it comes to “zero-calorie” foods, and broccoli is one of the best ones for you. Its deep-green hue is a dead giveaway that it’s full of cancer-fighting phytochemicals. Broccoli is also packed with gut-filling fiber and a surprising amount of protein for a vegetable (2.6 grams in one cup).
5: PURPLE CABBAGE
Calories per cup, chopped: 22
Purple foods are particularly good at protecting the heart. While you probably won’t be biting into a whole cabbage, cut off a big chunk and toss it into salads or stir-fries. The addition will make your meal more filling and help curb the need to snack as the day goes on.
6: CAULIFLOWER
Calories per cup, chopped: 27
If you are not so keen on the taste of raw cauliflower, try steaming and flavoring it with herbs and spices or a little lemon zest.
7: CHERRY TOMATOES
Calories per cup: 27
This is the only “fruit” you’ll see on this list. In general, fruit has about 60 calories per serving more than double the calorie content of non-starchy vegetables. But tomatoes are on the lower end of the fruit calorie spectrum, so forgo grapes every now and then and pop cherry tomatoes into your mouth for a healthy, lower-sugar snack.
8: SPINACH
Calories per cup: 7
A powerful source of antioxidants, spinach is also packed with muscle- building iron.
Monday 25 April 2016
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